MJOLNIR_04_CovidMisconception
Léo HENRY
4/29/2022
library(questionr)
library(FactoMineR)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:questionr':
##
## describelibrary(GPArotation)
library(FactoMineR)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(explor)
options(max.print=5001)data
df <- read.csv("./data/mjolnir_clean_v5_AMBI_BIG5.csv")COV variables
df %>% dplyr::select(matches("^COV((ATT)|(CONSP)|(ORIGIN)|(POLITICS)|(COVERAGE)|(ANTIVACC)|(MEDSKEP))"))## COVATT_1 COVATT_2 COVATT_3 COVATT_4 COVATT_5 COVCONSP_1 COVCONSP_2
## 1 1 2 3 4 3 3 3
## 2 3 2 1 1 4 3 1
## 3 1 6 1 1 6 1 1
## 4 2 5 2 1 4 1 1
## 5 1 4 2 2 4 1 2
## 6 2 1 1 2 3 1 4
## 7 2 4 1 1 6 1 4
## 8 1 6 2 1 6 6 5
## 9 1 3 1 1 5 1 3
## 10 3 1 2 3 4 1 1
## 11 5 1 4 3 3 1 1
## 12 6 2 1 1 6 1 1
## 13 2 3 1 1 5 1 1
## 14 3 1 2 1 3 1 3
## 15 4 2 4 3 6 1 1
## 16 2 2 1 1 4 2 2
## 17 2 1 2 2 4 1 1
## 18 4 1 1 1 5 4 6
## 19 3 1 1 2 3 4 1
## 20 4 3 1 2 1 1 4
## 21 4 1 4 2 4 1 2
## 22 5 2 1 2 6 1 2
## 23 3 1 1 1 4 2 1
## 24 2 2 2 2 2 1 1
## 25 3 3 1 1 4 1 3
## 26 2 2 1 2 5 2 1
## 27 1 3 2 1 4 1 2
## 28 1 2 1 1 5 1 4
## 29 1 2 1 1 5 1 2
## 30 1 1 1 2 2 1 2
## 31 1 1 6 2 6 1 1
## 32 2 4 2 2 4 3 2
## 33 3 1 3 4 2 1 2
## 34 1 5 1 1 6 1 3
## 35 5 2 2 4 4 1 4
## 36 1 1 2 1 5 1 2
## 37 1 6 1 1 3 1 1
## 38 2 5 2 1 3 1 1
## 39 1 1 1 1 4 2 1
## 40 2 5 1 1 4 1 1
## 41 1 4 1 1 6 1 1
## 42 2 2 1 3 4 1 1
## 43 1 3 1 1 6 1 1
## 44 2 1 1 2 1 1 5
## 45 3 1 4 3 5 2 2
## 46 2 1 1 3 1 1 2
## 47 5 2 2 3 4 1 1
## 48 2 4 4 3 3 1 4
## 49 2 1 5 1 1 6 6
## 50 6 1 2 1 5 1 1
## 51 1 5 1 1 6 1 2
## 52 3 2 2 5 5 3 2
## 53 2 2 2 1 4 1 3
## 54 2 2 1 2 6 1 2
## 55 1 4 1 1 6 1 1
## 56 1 1 1 1 6 1 3
## 57 2 4 1 1 6 4 3
## 58 2 1 1 2 6 1 1
## 59 1 4 2 1 4 1 4
## 60 1 2 1 1 6 1 1
## 61 1 2 1 1 6 1 1
## 62 1 2 1 2 2 1 3
## 63 4 3 4 5 2 2 3
## 64 2 2 4 3 4 2 4
## 65 1 1 1 1 3 1 4
## 66 2 1 1 4 3 1 1
## 67 4 1 2 2 1 1 4
## 68 1 1 1 1 4 1 1
## 69 4 2 4 4 3 1 2
## 70 3 2 1 1 4 6 3
## 71 4 1 3 3 5 2 2
## 72 3 1 1 4 2 1 4
## 73 2 2 2 1 6 3 2
## 74 1 3 1 1 6 1 4
## 75 5 3 1 1 4 1 5
## 76 1 3 2 1 5 1 1
## 77 2 4 1 1 5 1 4
## 78 2 1 3 3 2 1 4
## 79 1 1 1 2 6 1 1
## 80 2 4 5 3 5 2 4
## 81 5 2 1 2 2 1 1
## 82 1 3 1 1 6 1 1
## 83 3 3 2 1 4 1 2
## 84 5 1 3 4 3 5 1
## 85 1 5 2 1 3 1 1
## 86 2 2 3 2 3 1 4
## 87 6 2 2 1 5 1 1
## 88 2 3 1 1 4 1 1
## 89 1 5 1 1 5 1 5
## 90 1 4 1 1 6 1 1
## 91 2 4 2 1 4 3 4
## 92 2 5 1 1 4 1 4
## 93 2 2 1 1 5 1 1
## 94 4 3 1 2 4 1 4
## 95 3 1 3 5 2 1 5
## 96 3 1 1 3 4 1 2
## 97 6 4 1 1 6 2 1
## 98 2 1 2 1 5 1 3
## 99 1 4 1 1 6 1 1
## 100 2 2 2 1 1 1 1
## 101 2 1 1 1 5 1 1
## 102 1 4 2 1 6 1 2
## 103 2 2 3 1 1 4 3
## 104 1 4 1 1 6 1 1
## 105 1 4 1 1 4 1 3
## 106 6 1 4 4 5 3 4
## 107 2 1 2 3 5 1 4
## 108 3 2 3 2 2 1 5
## 109 2 1 4 2 2 2 3
## 110 4 3 2 4 6 4 2
## 111 2 1 2 2 5 1 2
## 112 1 6 1 1 6 1 1
## 113 3 2 1 1 4 1 1
## 114 1 4 1 1 1 4 3
## 115 1 3 1 3 6 1 3
## 116 2 1 2 2 3 1 3
## 117 1 1 1 1 6 2 1
## 118 3 2 3 3 2 1 1
## 119 1 6 1 1 5 1 3
## 120 3 1 5 1 5 1 3
## 121 2 1 1 1 6 1 4
## 122 1 2 2 3 5 1 2
## 123 2 3 4 3 3 4 2
## 124 1 1 1 2 1 4 1
## 125 1 3 1 1 6 2 3
## 126 6 5 1 1 6 2 4
## 127 2 4 3 2 5 1 2
## 128 3 4 3 2 4 1 1
## 129 2 5 2 1 4 1 1
## 130 6 1 4 4 3 4 5
## 131 4 2 5 4 2 1 1
## 132 1 4 1 1 6 1 1
## 133 5 2 5 3 3 4 4
## 134 4 1 1 1 1 1 1
## 135 5 4 3 2 4 1 1
## 136 4 2 1 1 3 1 4
## 137 2 3 1 1 5 1 1
## 138 5 3 4 3 2 3 2
## 139 1 2 1 1 5 1 4
## 140 1 3 1 1 4 1 2
## 141 1 3 2 2 4 1 3
## 142 2 2 2 1 1 2 4
## 143 1 3 3 2 6 1 5
## 144 1 4 1 1 5 3 3
## 145 1 6 1 1 1 1 1
## 146 1 3 1 1 3 1 6
## 147 1 1 1 1 5 2 3
## 148 1 4 1 1 6 1 1
## 149 3 1 2 3 4 3 4
## 150 3 2 1 2 4 1 4
## 151 3 3 1 1 5 1 1
## 152 2 5 1 2 4 1 1
## 153 1 4 1 1 6 6 6
## 154 3 1 2 1 4 4 3
## 155 1 2 4 2 5 1 1
## 156 1 3 1 1 4 1 3
## 157 5 4 1 1 2 4 3
## 158 2 3 3 1 2 1 2
## 159 1 1 1 1 5 2 1
## 160 2 5 1 1 5 1 1
## 161 1 4 3 2 6 1 3
## 162 2 3 2 2 5 2 1
## 163 4 1 5 3 4 4 5
## 164 5 1 5 6 4 2 4
## 165 3 2 1 1 4 3 3
## 166 1 1 1 1 6 1 1
## 167 1 2 1 1 2 1 5
## 168 2 5 1 1 4 1 4
## 169 3 1 1 3 5 1 2
## 170 2 4 1 1 6 2 2
## 171 3 4 2 2 6 4 4
## 172 2 3 2 3 3 1 1
## 173 1 4 1 1 6 1 4
## 174 1 3 2 1 4 1 3
## 175 4 1 1 1 3 1 1
## 176 2 4 2 3 4 2 3
## 177 1 1 2 4 6 2 2
## 178 1 1 1 1 1 1 3
## 179 2 2 1 1 5 1 3
## 180 2 1 2 1 4 1 3
## 181 1 2 3 1 5 1 1
## 182 4 1 3 3 2 1 1
## 183 1 4 3 1 6 1 1
## 184 3 4 3 5 2 2 3
## 185 1 4 1 1 6 1 3
## 186 1 5 1 2 2 3 3
## 187 1 4 2 2 5 1 2
## 188 1 4 1 1 6 1 1
## 189 2 2 1 1 3 2 6
## 190 3 4 2 2 4 3 4
## 191 4 1 2 1 6 1 1
## 192 2 5 3 2 4 2 4
## COVCONSP_3 COVCONSP_4 COVORIGIN_1 COVORIGIN_2 COVORIGIN_3 COVORIGIN_4
## 1 4 2 1 3 3 3
## 2 1 6 1 6 1 1
## 3 1 6 1 6 1 1
## 4 1 5 1 5 1 2
## 5 1 6 1 4 3 2
## 6 2 6 2 6 2 5
## 7 4 6 1 4 1 2
## 8 5 4 5 2 5 3
## 9 3 1 1 4 4 1
## 10 1 6 1 4 3 2
## 11 1 6 1 6 1 1
## 12 1 6 2 4 6 5
## 13 1 6 1 4 2 1
## 14 1 6 1 6 1 2
## 15 3 6 6 3 5 5
## 16 3 2 5 2 5 6
## 17 1 6 1 6 1 2
## 18 4 1 4 4 2 5
## 19 3 4 1 5 2 4
## 20 1 1 2 4 3 2
## 21 2 6 2 4 1 1
## 22 1 6 3 2 5 3
## 23 2 4 1 6 1 1
## 24 1 6 1 6 1 1
## 25 3 6 1 6 1 1
## 26 1 2 3 3 5 3
## 27 1 6 2 4 2 4
## 28 1 6 2 5 2 1
## 29 2 6 1 5 1 1
## 30 1 6 1 6 1 1
## 31 1 6 1 6 1 1
## 32 2 4 2 5 2 2
## 33 3 3 2 3 4 3
## 34 2 6 1 6 1 1
## 35 4 2 4 4 5 3
## 36 1 6 1 6 1 1
## 37 1 6 2 3 4 1
## 38 1 6 1 6 1 2
## 39 2 4 3 4 3 5
## 40 1 6 1 4 1 1
## 41 1 6 1 6 1 1
## 42 1 6 1 6 1 1
## 43 3 1 1 4 1 1
## 44 1 6 4 3 4 4
## 45 2 2 2 3 2 2
## 46 2 2 1 6 1 1
## 47 1 6 1 6 1 2
## 48 6 2 3 4 3 4
## 49 5 1 6 1 6 6
## 50 1 6 1 6 1 1
## 51 1 6 2 4 2 3
## 52 1 5 1 5 1 2
## 53 1 5 1 6 1 1
## 54 1 6 2 4 3 1
## 55 1 6 1 5 2 1
## 56 2 5 1 6 1 3
## 57 3 5 2 4 3 2
## 58 1 6 1 4 1 1
## 59 4 6 4 4 4 3
## 60 1 6 1 5 3 2
## 61 2 6 1 6 1 1
## 62 2 4 4 1 5 4
## 63 4 3 4 3 4 4
## 64 2 4 1 4 3 5
## 65 4 3 1 6 1 1
## 66 1 5 2 4 2 4
## 67 4 1 5 2 6 5
## 68 1 6 1 6 1 1
## 69 1 3 2 5 3 4
## 70 5 1 3 4 3 3
## 71 1 5 5 3 4 5
## 72 4 1 2 2 4 3
## 73 3 2 4 2 5 4
## 74 1 6 2 3 4 3
## 75 2 1 1 5 1 1
## 76 1 3 1 5 1 4
## 77 1 5 2 4 2 2
## 78 3 1 2 2 6 4
## 79 1 6 1 6 1 1
## 80 3 5 2 4 1 1
## 81 2 5 1 6 1 1
## 82 1 3 2 5 2 1
## 83 1 6 1 4 1 3
## 84 1 2 1 5 1 1
## 85 1 6 1 5 1 1
## 86 4 6 2 5 2 2
## 87 1 3 1 2 1 1
## 88 1 6 1 6 1 1
## 89 2 3 3 4 3 5
## 90 1 6 1 5 2 1
## 91 4 4 4 3 4 3
## 92 1 2 1 6 1 1
## 93 1 6 1 6 1 3
## 94 3 6 1 5 1 1
## 95 6 3 1 5 2 2
## 96 1 4 4 3 4 4
## 97 1 6 1 5 4 1
## 98 2 4 3 3 4 3
## 99 1 6 3 4 4 4
## 100 1 6 1 6 1 1
## 101 3 4 1 5 2 2
## 102 2 5 1 6 1 1
## 103 4 3 3 4 3 3
## 104 1 6 1 5 2 1
## 105 5 6 1 5 1 4
## 106 3 5 1 4 1 6
## 107 3 6 1 5 1 3
## 108 1 6 4 4 3 3
## 109 3 5 5 1 6 5
## 110 1 6 2 4 3 4
## 111 1 6 1 6 1 1
## 112 1 6 1 4 3 2
## 113 1 1 1 3 1 1
## 114 2 5 1 2 1 1
## 115 2 6 4 3 3 4
## 116 1 6 2 5 2 4
## 117 1 4 4 2 4 3
## 118 1 6 4 3 4 3
## 119 1 6 1 5 2 1
## 120 2 3 1 5 2 1
## 121 1 6 3 4 5 6
## 122 2 6 1 6 1 1
## 123 3 4 4 3 2 3
## 124 2 5 1 2 5 4
## 125 2 1 1 1 1 2
## 126 5 3 3 4 3 4
## 127 1 2 1 2 2 4
## 128 2 6 3 4 3 4
## 129 1 4 1 5 2 1
## 130 2 6 5 6 4 5
## 131 1 2 2 2 4 2
## 132 1 5 1 6 1 1
## 133 4 3 3 4 3 3
## 134 1 6 1 6 1 1
## 135 1 6 1 6 1 1
## 136 1 5 4 4 4 5
## 137 1 6 1 5 1 1
## 138 2 1 2 5 3 3
## 139 1 6 2 6 2 1
## 140 2 3 2 5 3 2
## 141 3 1 1 5 2 1
## 142 1 6 1 2 4 4
## 143 1 6 1 6 1 1
## 144 4 4 1 6 1 1
## 145 1 6 1 6 1 1
## 146 6 1 1 6 1 1
## 147 1 4 1 5 1 1
## 148 1 6 1 6 1 4
## 149 4 4 2 3 4 4
## 150 1 6 3 4 3 2
## 151 1 6 2 4 2 1
## 152 1 5 1 4 2 2
## 153 6 1 5 3 4 1
## 154 5 1 2 4 4 4
## 155 6 1 6 1 6 4
## 156 1 4 1 2 1 1
## 157 3 5 4 2 4 4
## 158 2 5 2 5 2 4
## 159 2 6 1 5 2 6
## 160 2 5 1 5 2 2
## 161 1 5 1 4 1 4
## 162 2 5 2 5 2 2
## 163 6 1 2 5 2 1
## 164 5 4 3 4 5 5
## 165 3 5 2 4 3 3
## 166 1 6 1 6 1 1
## 167 4 4 2 4 2 2
## 168 3 5 1 5 2 2
## 169 2 4 1 4 4 2
## 170 2 6 1 5 2 4
## 171 2 6 1 6 1 1
## 172 4 3 4 3 4 4
## 173 1 6 2 5 2 4
## 174 2 3 1 5 2 2
## 175 1 6 1 3 3 5
## 176 4 4 2 4 2 3
## 177 2 1 1 5 1 1
## 178 2 6 1 6 1 1
## 179 3 5 2 5 1 1
## 180 2 1 3 4 3 3
## 181 1 1 4 3 4 2
## 182 1 6 4 3 3 4
## 183 1 4 1 4 2 1
## 184 3 4 3 4 2 4
## 185 1 6 1 5 2 3
## 186 3 4 2 3 4 2
## 187 2 5 1 6 1 3
## 188 1 5 3 3 4 4
## 189 3 5 4 2 5 4
## 190 4 2 2 5 2 3
## 191 2 2 2 5 2 1
## 192 4 4 1 4 1 3
## COVPOLITICS_1 COVPOLITICS_2 COVPOLITICS_3 COVCOVERAGE_1 COVCOVERAGE_2
## 1 2 2 3 2 4
## 2 6 1 6 1 6
## 3 1 1 6 5 2
## 4 1 1 1 1 3
## 5 3 3 4 4 4
## 6 6 5 2 6 3
## 7 3 3 5 2 1
## 8 6 2 3 3 5
## 9 1 1 1 1 4
## 10 6 3 3 4 4
## 11 1 2 5 2 5
## 12 1 1 6 1 3
## 13 2 1 6 2 2
## 14 4 2 6 2 4
## 15 4 4 4 6 4
## 16 6 3 5 4 3
## 17 1 1 4 3 2
## 18 6 2 4 3 6
## 19 4 5 6 4 6
## 20 6 4 6 6 2
## 21 4 3 3 4 4
## 22 6 3 5 4 2
## 23 6 1 6 3 4
## 24 6 2 6 4 1
## 25 3 1 6 2 5
## 26 4 2 5 2 2
## 27 4 2 6 3 4
## 28 1 1 5 1 2
## 29 3 2 5 3 4
## 30 1 1 6 1 5
## 31 5 1 5 5 4
## 32 3 3 5 3 4
## 33 3 3 4 3 5
## 34 1 1 6 1 4
## 35 6 2 2 4 4
## 36 1 1 6 1 4
## 37 1 1 6 1 5
## 38 4 1 6 2 4
## 39 5 2 5 3 4
## 40 2 2 6 3 4
## 41 1 1 6 3 2
## 42 6 2 6 4 3
## 43 1 1 4 1 4
## 44 4 3 5 3 4
## 45 4 4 2 2 2
## 46 6 1 6 1 3
## 47 4 2 5 4 3
## 48 6 4 6 4 4
## 49 6 4 6 4 5
## 50 5 1 6 1 4
## 51 4 1 6 1 2
## 52 4 3 2 4 3
## 53 1 1 6 1 4
## 54 5 1 5 2 4
## 55 1 1 6 4 3
## 56 1 1 4 1 4
## 57 3 1 5 2 5
## 58 2 1 5 1 5
## 59 3 2 4 2 4
## 60 4 4 3 4 5
## 61 1 1 6 1 6
## 62 4 4 5 4 2
## 63 3 6 3 6 4
## 64 4 3 4 3 4
## 65 4 4 5 4 3
## 66 5 3 6 2 6
## 67 4 5 2 5 2
## 68 2 1 6 3 4
## 69 4 5 3 6 3
## 70 5 5 4 5 4
## 71 6 2 4 3 2
## 72 6 4 4 6 2
## 73 5 1 5 2 4
## 74 3 1 6 3 4
## 75 2 2 5 3 4
## 76 6 3 6 3 4
## 77 4 3 6 4 3
## 78 6 6 2 6 3
## 79 1 1 6 1 5
## 80 1 1 1 5 2
## 81 4 3 5 3 6
## 82 1 1 6 2 5
## 83 5 4 6 5 5
## 84 1 4 3 3 3
## 85 1 1 6 1 1
## 86 4 3 5 3 3
## 87 1 4 6 2 4
## 88 5 1 6 4 5
## 89 4 3 5 4 2
## 90 3 1 6 1 2
## 91 4 2 5 3 4
## 92 4 1 4 1 5
## 93 4 3 4 4 5
## 94 4 2 5 6 3
## 95 6 5 2 6 4
## 96 5 3 5 3 3
## 97 5 1 6 1 3
## 98 4 4 5 4 4
## 99 1 1 6 1 3
## 100 4 2 4 4 4
## 101 1 1 5 1 4
## 102 1 1 6 1 5
## 103 3 2 5 3 5
## 104 1 1 6 1 3
## 105 4 1 6 3 5
## 106 6 3 5 4 4
## 107 4 4 6 5 5
## 108 5 4 2 4 2
## 109 5 6 3 3 4
## 110 6 1 6 3 4
## 111 2 2 5 2 3
## 112 1 1 6 1 4
## 113 4 2 6 4 3
## 114 4 1 6 1 3
## 115 4 1 6 2 5
## 116 2 1 5 2 2
## 117 6 1 5 4 4
## 118 4 4 3 5 4
## 119 1 1 6 1 4
## 120 4 3 6 3 3
## 121 6 4 5 6 4
## 122 4 1 6 2 2
## 123 3 5 3 4 2
## 124 6 2 5 2 4
## 125 6 1 6 2 4
## 126 3 1 6 3 4
## 127 6 2 5 6 5
## 128 4 5 6 5 3
## 129 6 1 5 2 3
## 130 6 6 4 6 5
## 131 5 5 2 5 4
## 132 2 1 6 3 2
## 133 4 5 3 5 3
## 134 6 1 6 5 3
## 135 5 2 6 4 3
## 136 6 3 6 5 3
## 137 2 2 6 2 3
## 138 5 4 4 4 2
## 139 1 1 6 1 6
## 140 2 2 5 2 2
## 141 4 3 6 3 4
## 142 3 4 5 4 3
## 143 6 1 6 1 2
## 144 2 2 6 2 5
## 145 1 1 6 1 6
## 146 4 1 6 1 2
## 147 4 2 5 5 4
## 148 4 2 6 3 5
## 149 6 4 4 4 3
## 150 4 2 5 2 4
## 151 4 3 5 4 4
## 152 5 2 5 2 3
## 153 1 1 6 1 1
## 154 6 6 6 6 1
## 155 3 2 4 2 1
## 156 2 1 4 2 4
## 157 3 2 4 2 2
## 158 2 2 5 2 4
## 159 4 1 6 1 5
## 160 4 1 6 3 4
## 161 6 4 4 5 6
## 162 2 2 5 2 5
## 163 6 6 1 6 1
## 164 4 6 4 6 3
## 165 4 3 4 4 3
## 166 1 1 6 1 6
## 167 3 1 6 1 3
## 168 2 2 5 2 4
## 169 3 2 6 4 4
## 170 3 2 5 3 4
## 171 6 2 5 3 4
## 172 6 4 5 4 2
## 173 3 3 6 2 5
## 174 3 2 6 2 4
## 175 5 4 4 4 3
## 176 4 4 4 4 4
## 177 6 3 5 4 3
## 178 6 3 6 6 5
## 179 1 2 5 4 4
## 180 4 2 6 3 5
## 181 2 2 5 3 5
## 182 5 6 4 5 3
## 183 6 1 5 3 4
## 184 4 4 4 4 3
## 185 1 1 6 2 2
## 186 4 2 5 3 4
## 187 4 1 6 2 4
## 188 4 1 6 1 3
## 189 4 1 6 2 5
## 190 2 2 4 3 4
## 191 4 4 6 4 5
## 192 4 2 6 4 4
## COVCOVERAGE_3 COVANTIVACC_1 COVANTIVACC_2 COVANTIVACC_3 COVMEDSKEP_1
## 1 4 3 3 4 4
## 2 1 3 1 6 1
## 3 1 1 1 6 1
## 4 3 1 2 5 1
## 5 3 1 1 6 2
## 6 1 5 3 4 2
## 7 2 2 2 5 1
## 8 3 5 1 3 3
## 9 1 1 1 6 3
## 10 4 5 3 3 3
## 11 3 3 1 3 2
## 12 2 1 1 6 5
## 13 1 2 1 6 1
## 14 2 1 2 4 1
## 15 4 4 5 1 5
## 16 5 6 4 3 5
## 17 3 1 1 6 1
## 18 3 1 1 6 4
## 19 3 4 1 6 3
## 20 3 5 2 5 6
## 21 3 3 1 4 1
## 22 5 2 2 6 2
## 23 3 2 1 5 1
## 24 1 1 1 6 1
## 25 1 1 1 6 1
## 26 1 2 1 5 3
## 27 2 1 1 6 3
## 28 1 1 2 4 2
## 29 3 1 1 6 1
## 30 1 1 1 6 1
## 31 2 1 1 6 1
## 32 3 3 1 5 1
## 33 3 2 1 6 1
## 34 1 1 1 6 1
## 35 3 6 4 3 6
## 36 3 1 1 6 1
## 37 1 1 1 5 1
## 38 1 1 1 6 1
## 39 2 5 1 6 2
## 40 3 2 1 5 1
## 41 1 1 1 6 2
## 42 1 1 1 4 2
## 43 1 1 1 4 3
## 44 2 2 2 5 3
## 45 2 3 4 4 5
## 46 1 1 1 6 1
## 47 1 4 1 5 3
## 48 3 4 2 5 3
## 49 4 6 4 3 4
## 50 2 1 1 6 1
## 51 1 2 1 6 1
## 52 3 3 1 4 4
## 53 1 1 1 6 1
## 54 4 2 1 5 1
## 55 1 1 1 6 2
## 56 3 1 1 6 1
## 57 3 2 1 6 1
## 58 1 1 1 5 1
## 59 2 2 2 5 1
## 60 1 3 1 6 1
## 61 1 1 1 6 1
## 62 3 6 3 2 2
## 63 3 4 3 3 6
## 64 2 2 1 5 2
## 65 1 1 1 5 3
## 66 1 5 1 6 3
## 67 4 3 1 2 4
## 68 2 1 1 6 1
## 69 4 3 2 5 3
## 70 4 5 1 5 3
## 71 3 2 1 3 2
## 72 5 5 2 4 4
## 73 2 5 2 5 4
## 74 4 4 3 4 2
## 75 4 1 1 6 1
## 76 4 4 4 6 3
## 77 2 2 1 6 1
## 78 4 2 1 6 5
## 79 1 1 1 6 1
## 80 3 2 1 5 1
## 81 1 2 1 6 6
## 82 1 1 1 6 5
## 83 2 1 1 6 1
## 84 4 1 1 6 3
## 85 1 1 1 6 1
## 86 3 1 1 5 3
## 87 1 1 1 5 2
## 88 4 1 1 6 1
## 89 4 4 2 3 4
## 90 1 1 1 6 1
## 91 3 3 1 5 3
## 92 2 1 1 6 1
## 93 3 4 3 2 2
## 94 3 2 2 4 4
## 95 6 2 1 6 4
## 96 4 2 1 3 3
## 97 2 1 1 6 3
## 98 3 3 2 5 3
## 99 1 3 1 4 1
## 100 4 2 2 4 1
## 101 3 1 1 6 1
## 102 1 1 1 6 1
## 103 2 2 1 6 1
## 104 1 3 1 5 1
## 105 1 4 1 6 1
## 106 2 2 1 4 4
## 107 6 1 2 5 4
## 108 3 4 1 3 3
## 109 3 6 6 2 6
## 110 2 2 1 5 2
## 111 1 1 1 5 1
## 112 1 1 1 6 1
## 113 2 1 1 6 1
## 114 5 2 1 6 1
## 115 1 1 1 6 2
## 116 1 2 1 6 1
## 117 4 2 1 6 1
## 118 3 4 1 6 3
## 119 1 1 1 6 1
## 120 2 2 1 6 1
## 121 4 4 1 6 4
## 122 1 1 1 6 1
## 123 3 4 2 3 3
## 124 4 5 1 6 4
## 125 1 1 1 6 1
## 126 2 3 3 4 3
## 127 6 1 1 5 2
## 128 5 5 4 3 4
## 129 4 1 1 5 1
## 130 3 6 3 4 2
## 131 3 1 1 6 3
## 132 1 1 1 6 1
## 133 4 3 3 4 5
## 134 1 1 1 6 3
## 135 2 1 1 6 1
## 136 2 1 1 5 2
## 137 2 2 1 6 1
## 138 4 5 3 2 2
## 139 1 1 1 4 2
## 140 2 1 1 6 1
## 141 3 2 2 5 2
## 142 4 5 1 5 2
## 143 1 4 1 6 1
## 144 1 2 1 6 2
## 145 1 2 1 4 1
## 146 1 1 1 5 1
## 147 3 2 1 2 1
## 148 2 2 1 5 4
## 149 3 2 2 5 2
## 150 2 1 1 6 2
## 151 3 2 1 5 1
## 152 1 1 1 5 1
## 153 1 4 1 6 5
## 154 6 4 1 4 4
## 155 6 1 1 4 3
## 156 1 1 1 5 1
## 157 3 5 5 3 3
## 158 2 1 1 5 1
## 159 3 1 1 6 1
## 160 2 2 1 6 3
## 161 1 1 2 4 1
## 162 2 2 2 5 4
## 163 6 1 1 6 4
## 164 4 5 5 4 6
## 165 3 1 1 5 2
## 166 1 1 1 6 1
## 167 3 2 1 6 1
## 168 1 1 2 4 3
## 169 3 4 1 6 3
## 170 3 3 1 5 2
## 171 2 3 1 6 1
## 172 5 5 2 5 5
## 173 3 2 1 6 1
## 174 1 1 1 6 2
## 175 3 1 1 5 5
## 176 5 3 3 4 3
## 177 2 1 1 6 1
## 178 4 2 1 6 1
## 179 4 1 1 5 2
## 180 3 2 1 4 2
## 181 4 5 2 4 4
## 182 3 2 3 2 3
## 183 3 2 1 6 3
## 184 4 4 4 3 4
## 185 2 1 1 5 4
## 186 3 3 2 5 4
## 187 5 1 1 6 1
## 188 2 1 1 6 3
## 189 2 4 1 6 1
## 190 4 2 2 4 3
## 191 4 1 1 6 2
## 192 2 5 3 4 3
## COVMEDSKEP_2 COVMEDSKEP_3 COVMEDSKEP_4
## 1 3 4 4
## 2 1 6 6
## 3 1 6 6
## 4 2 1 3
## 5 1 6 6
## 6 2 5 5
## 7 2 5 5
## 8 4 5 5
## 9 1 4 5
## 10 3 3 3
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## 179 2 4 4
## 180 2 3 6
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## 182 2 4 4
## 183 1 4 6
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## 187 1 6 6
## 188 3 3 5
## 189 2 4 6
## 190 3 4 4
## 191 1 5 5
## 192 4 4 5
## [ reached 'max' / getOption("max.print") -- omitted 470 rows ]df_covmis <- df %>%
mutate(covmis_att_flu=COVATT_1,
covmis_att_afrDie=7-COVATT_2,
covmis_att_eldrNoBgDl=COVATT_3,
covmis_att_rareNoWorr=COVATT_4,
covmis_att_bgThrt=7-COVATT_5,
covmis_cnsp_ctiusAsian=COVCONSP_1,
covmis_cnsp_stpCovStpImmi=COVCONSP_2,
covmis_cnsp_redIntWthChina=COVCONSP_3,
covmis_cnsp_chnsCovRcst=7-COVCONSP_4,
covmis_orgn_covPlnnd=COVORIGIN_1,
covmis_orgn_covNat=7-COVORIGIN_2,
covmis_orgn_covNgeenLab=COVORIGIN_3,
covmis_orgn_scntFkNwsCov=COVORIGIN_4,
covmis_pltc_polBgDlIntrst=COVPOLITICS_1,
covmis_pltc_covNtSerPolSay=COVPOLITICS_2,
covmis_pltc_polDwnplCovPlpLDngr=7-COVPOLITICS_3,
covmis_cvrg_mdiaCovBgrDl=COVCOVERAGE_1,
covmis_cvrg_nwsGdJbComCov=7-COVCOVERAGE_2,
covmis_cvrg_mdiaUseCovMkTrmpRepLkBd=COVCOVERAGE_3,
covmis_anti_frGovUseCovMndtVacc=COVANTIVACC_1,
covmis_anti_thnksNoCovVacc=COVANTIVACC_2,
covmis_anti_covVacEffRedVirus=7-COVANTIVACC_3,
covmis_mdsk_medOrgUntrust=COVMEDSKEP_1,
covmis_mdsk_skeptInfoDocSci=COVMEDSKEP_2,
covmis_mdsk_medOrgRecBstInt=7-COVMEDSKEP_3,
covmis_mdsk_fllwRecMedOrgImp=7-COVMEDSKEP_4)PCA analysis
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_att")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_cnsp")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_orgn")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_pltc")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_cvrg")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_anti")),
scale.unit=TRUE, ncp=25, graph=T)
#explor(res.pca)HCPM ET CLASSIF
res.mfa <- MFA(df_covmis %>% dplyr::select(matches("^covmis")) %>% rename_with(function(x){str_remove(x,"^covmis_[A-z]{3,4}_")}),
group = c(5,4,4,3,3,3,4),
type=c("c","c","c","c","c","c","c"),
name.group = c("att","conspiracy","origin","politics","coverage","antivaccin", "Mdsk"))
## Warning: ggrepel: 655 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 608 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
get_eigenvalue(res.mfa) %>% head## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 4.1453673 37.179358 37.17936
## Dim.2 0.9929282 8.905467 46.08482
## Dim.3 0.6763359 6.065985 52.15081
## Dim.4 0.5696746 5.109351 57.26016
## Dim.5 0.5318857 4.770426 62.03059
## Dim.6 0.4620883 4.144421 66.17501fviz_screeplot(res.mfa)
group <- get_mfa_var(res.mfa, "group")
group$coord## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## att 0.4288261 0.400972540 0.02252065 0.117682630 0.332010787
## conspiracy 0.3687215 0.240858293 0.33687679 0.052382138 0.113129399
## origin 0.6423536 0.102705673 0.04241034 0.009136517 0.005009324
## politics 0.7064960 0.091408058 0.02478238 0.302679657 0.006263261
## coverage 0.7110741 0.063562788 0.04632540 0.041165098 0.032486902
## antivaccin 0.6358459 0.005753999 0.17000533 0.027384851 0.007699764
## Mdsk 0.6520501 0.087666845 0.03341500 0.019243732 0.035286238group$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## att 10.344708 40.382834 3.329803 20.657868 62.4214568
## conspiracy 8.894784 24.257373 49.809095 9.195098 21.2694953
## origin 15.495697 10.343716 6.270603 1.603813 0.9418047
## politics 17.043025 9.205908 3.664212 53.132024 1.1775578
## coverage 17.153465 6.401549 6.849467 7.226072 6.1078731
## antivaccin 15.338711 0.579498 25.136228 4.807104 1.4476352
## Mdsk 15.729609 8.829122 4.940592 3.378022 6.6341771# Contribution to the first dimension
fviz_contrib(res.mfa, "group", axes = 1)
# Contribution to the second dimension
fviz_contrib(res.mfa, "group", axes = 2)
quanti.var <- get_mfa_var(res.mfa, "quanti.var")
quanti.var$contrib## Dim.1 Dim.2 Dim.3 Dim.4
## flu 2.6064772 7.73560847 0.03709541 3.835763e+00
## afrDie 1.4959458 19.85699962 2.95902165 5.792656e+00
## eldrNoBgDl 1.6922641 1.67308477 0.03383058 2.854280e+00
## rareNoWorr 2.1734152 2.34024908 0.01092408 2.044521e+00
## bgThrt 2.3766055 8.77689160 0.28893155 6.130648e+00
## ctiusAsian 0.4727276 2.49084690 1.75270757 3.877927e-04
## stpCovStpImmi 1.7992285 6.00949577 14.24358834 7.331952e-02
## redIntWthChina 2.4372337 11.78788718 11.73464218 9.316842e-01
## chnsCovRcst 4.1855945 3.96914287 22.07815712 8.189706e+00
## covPlnnd 3.2349186 2.17350639 1.55211840 2.988802e-02
## covNat 3.0805566 4.32929552 1.28388754 3.772871e-06
## covNgeenLab 4.5029796 3.57720223 1.45014403 3.008399e-01
## scntFkNwsCov 4.6772426 0.26371186 1.98445253 1.273082e+00
## polBgDlIntrst 7.2656494 4.03509071 3.32272405 4.793314e+01
## covNtSerPolSay 6.4381947 3.51677321 0.30856562 1.018422e-01
## polDwnplCovPlpLDngr 3.3391812 1.65404422 0.03292261 5.097044e+00
## mdiaCovBgrDl 9.1007133 6.21123408 2.26851047 3.281355e+00
## nwsGdJbComCov 0.9491219 0.02372072 0.64815352 8.603278e-02
## mdiaUseCovMkTrmpRepLkBd 7.1036297 0.16659452 3.93280255 3.858684e+00
## frGovUseCovMndtVacc 7.8442930 0.23524817 10.16925338 2.079372e+00
## thnksNoCovVacc 2.9838512 0.22695275 5.74970514 2.579411e-01
## covVacEffRedVirus 4.5105670 0.11729709 9.21726956 2.469791e+00
## medOrgUntrust 4.7580788 3.99357954 1.01615181 6.491236e-01
## skeptInfoDocSci 4.1506347 1.73385579 2.23444151 1.448116e-03
## medOrgRecBstInt 3.5734929 2.12130754 0.83305724 9.003195e-01
## fllwRecMedOrgImp 3.2474025 0.98037941 0.85694156 1.827130e+00
## Dim.5
## flu 4.190999e+01
## afrDie 1.271874e+01
## eldrNoBgDl 2.697937e+00
## rareNoWorr 2.023975e+00
## bgThrt 3.070812e+00
## ctiusAsian 2.742445e+00
## stpCovStpImmi 5.901999e+00
## redIntWthChina 4.143984e+00
## chnsCovRcst 8.481068e+00
## covPlnnd 2.670275e-02
## covNat 2.664329e-01
## covNgeenLab 5.575524e-01
## scntFkNwsCov 9.111669e-02
## polBgDlIntrst 2.780905e-01
## covNtSerPolSay 7.117344e-01
## polDwnplCovPlpLDngr 1.877329e-01
## mdiaCovBgrDl 3.315568e-04
## nwsGdJbComCov 5.875296e+00
## mdiaUseCovMkTrmpRepLkBd 2.322459e-01
## frGovUseCovMndtVacc 5.437454e-01
## thnksNoCovVacc 8.992008e-01
## covVacEffRedVirus 4.689018e-03
## medOrgUntrust 2.072189e+00
## skeptInfoDocSci 1.833596e-02
## medOrgRecBstInt 3.693040e+00
## fllwRecMedOrgImp 8.506115e-01quanti.var$coord## Dim.1 Dim.2 Dim.3 Dim.4
## flu 0.6527693 -0.55037335 -0.03145520 0.2935557090
## afrDie 0.4945278 -0.88179366 0.28093544 0.3607476911
## eldrNoBgDl 0.5259771 -0.25595825 0.03003912 0.2532287182
## rareNoWorr 0.5960794 -0.30272006 0.01706966 0.2143188992
## bgThrt 0.6233203 -0.58624685 -0.08778695 0.3711229863
## ctiusAsian 0.3054965 0.34320347 0.23760479 0.0032436390
## stpCovStpImmi 0.5959968 0.53308587 0.67734536 0.0446007833
## redIntWthChina 0.6936643 0.74661402 0.61480215 0.1589889850
## chnsCovRcst 0.9090323 0.43323798 0.84329954 0.4713753081
## covPlnnd 0.8207190 0.32924614 -0.22962806 -0.0292444414
## covNat 0.8008984 0.46467450 -0.20884593 0.0003285721
## covNgeenLab 0.9683064 0.42238839 -0.22195662 -0.0927817353
## scntFkNwsCov 0.9868650 0.11468459 -0.25964674 -0.1908634880
## polBgDlIntrst 1.1501782 -0.41950000 0.31417726 -1.0951604768
## covNtSerPolSay 1.0827045 -0.39163139 0.09574175 0.0504805096
## polDwnplCovPlpLDngr 0.7797366 -0.26858322 0.03127337 0.3571239876
## mdiaCovBgrDl 1.2257469 -0.49559791 0.24719131 -0.2728485478
## nwsGdJbComCov 0.3958440 -0.03062700 -0.13213010 0.0441801322
## mdiaUseCovMkTrmpRepLkBd 1.0829372 -0.08116539 0.32547219 -0.2958793399
## frGovUseCovMndtVacc 1.0549893 0.08941527 -0.48519398 -0.2013581254
## thnksNoCovVacc 0.6506684 0.08782462 -0.36483261 0.0709191191
## covVacEffRedVirus 0.7999940 0.06313820 -0.46192558 0.2194487867
## medOrgUntrust 0.9729381 0.43624251 -0.18161362 0.1332186491
## skeptInfoDocSci 0.9087128 0.28744403 -0.26931070 -0.0062922075
## medOrgRecBstInt 0.8431714 0.31794253 -0.16443965 0.1568915898
## fllwRecMedOrgImp 0.8037805 0.21614436 -0.16678029 0.2235044113
## Dim.5
## flu 0.937603389
## afrDie -0.516514418
## eldrNoBgDl 0.237890057
## rareNoWorr 0.206045408
## bgThrt -0.253797318
## ctiusAsian 0.263570819
## stpCovStpImmi 0.386658854
## redIntWthChina 0.323994317
## chnsCovRcst -0.463504171
## covPlnnd 0.026709667
## covNat -0.084369286
## covNgeenLab -0.122048737
## scntFkNwsCov -0.049338894
## polBgDlIntrst -0.080602515
## covNtSerPolSay 0.128947976
## polDwnplCovPlpLDngr 0.066225574
## mdiaCovBgrDl 0.002650145
## nwsGdJbComCov -0.352781339
## mdiaUseCovMkTrmpRepLkBd -0.070139844
## frGovUseCovMndtVacc 0.099493929
## thnksNoCovVacc 0.127946127
## covVacEffRedVirus 0.009239314
## medOrgUntrust -0.229991477
## skeptInfoDocSci 0.021634601
## medOrgRecBstInt -0.307035835
## fllwRecMedOrgImp -0.147354247fviz_mfa_var(res.mfa, "quanti.var", palette = "jco",
col.var.sup = "violet", repel = TRUE)
fviz_mfa_var(res.mfa, "quanti.var", palette = "jco",
col.var.sup = "violet", repel = TRUE,
geom = c("point", "text"), legend = "bottom")## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Contributions to dimension 1
fviz_contrib(res.mfa, choice = "quanti.var", axes = 1, top = 20,
palette = "jco")
# Contributions to dimension 2
fviz_contrib(res.mfa, choice = "quanti.var", axes = 2, top = 20,
palette = "jco")
Classification
res.hcpc <- HCPC(res.mfa, nb.clust=4, graph = T)
fviz_cluster(res.hcpc,
repel = TRUE,
show.clust.cent = TRUE,
palette = "jco",
ggtheme = theme_minimal(),
main = "Factor map"
)## Warning: ggrepel: 601 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
res.hcpc$desc.var$quanti## $`1`
## v.test Mean in category Overall mean sd in category
## nwsGdJbComCov -4.015687 3.007117 3.235650 1.2370119
## afrDie -5.239376 3.583630 3.950151 1.5259146
## flu -7.166642 1.722420 2.214502 1.4045538
## ctiusAsian -7.735925 1.153025 1.510574 0.4861788
## bgThrt -8.082505 1.939502 2.474320 1.1597821
## thnksNoCovVacc -8.859401 1.049822 1.451662 0.3643475
## eldrNoBgDl -8.970743 1.288256 1.712991 0.5773027
## stpCovStpImmi -9.945192 1.640569 2.291541 1.1011116
## covNat -10.021669 1.932384 2.569486 1.1836900
## covVacEffRedVirus -10.345263 1.398577 1.962236 0.6784938
## redIntWthChina -10.363849 1.494662 2.175227 0.9012486
## rareNoWorr -10.683372 1.138790 1.623867 0.3558719
## chnsCovRcst -10.936419 1.530249 2.377644 1.0637891
## polDwnplCovPlpLDngr -10.996744 1.249110 1.853474 0.6604277
## fllwRecMedOrgImp -12.016231 1.291815 1.903323 0.5661607
## covPlnnd -12.206609 1.131673 1.773414 0.3871978
## medOrgUntrust -12.409667 1.419929 2.222054 0.8057807
## medOrgRecBstInt -12.468806 1.494662 2.217523 0.8008936
## covNgeenLab -12.511153 1.402135 2.203927 0.7097659
## skeptInfoDocSci -12.649127 1.213523 1.906344 0.5820219
## scntFkNwsCov -12.902313 1.259786 2.054381 0.7209019
## frGovUseCovMndtVacc -13.013190 1.202847 2.045317 0.5578228
## covNtSerPolSay -14.654241 1.163701 2.074018 0.5007814
## mdiaUseCovMkTrmpRepLkBd -15.756154 1.338078 2.413897 0.7130770
## polBgDlIntrst -16.435821 1.950178 3.288520 1.3827901
## mdiaCovBgrDl -17.020175 1.423488 2.649547 0.6921136
## Overall sd p.value
## nwsGdJbComCov 1.256547 5.927280e-05
## afrDie 1.544582 1.611204e-07
## flu 1.516047 7.685970e-13
## ctiusAsian 1.020504 1.026537e-14
## bgThrt 1.461005 6.344998e-16
## thnksNoCovVacc 1.001474 8.044537e-19
## eldrNoBgDl 1.045396 2.945216e-19
## stpCovStpImmi 1.445240 2.646619e-23
## covNat 1.403655 1.224179e-23
## covVacEffRedVirus 1.203002 4.397085e-25
## redIntWthChina 1.449906 3.620910e-25
## rareNoWorr 1.002522 1.217668e-26
## chnsCovRcst 1.710810 7.718886e-28
## polDwnplCovPlpLDngr 1.213461 3.961795e-28
## fllwRecMedOrgImp 1.123636 2.919872e-33
## covPlnnd 1.160797 2.865882e-34
## medOrgUntrust 1.427163 2.316130e-35
## medOrgRecBstInt 1.280033 1.104688e-35
## covNgeenLab 1.414998 6.487553e-36
## skeptInfoDocSci 1.209350 1.131243e-36
## scntFkNwsCov 1.359782 4.367880e-38
## frGovUseCovMndtVacc 1.429428 1.029526e-38
## covNtSerPolSay 1.371579 1.265626e-48
## mdiaUseCovMkTrmpRepLkBd 1.507580 6.231180e-56
## polBgDlIntrst 1.797906 1.059796e-60
## mdiaCovBgrDl 1.590519 5.819639e-65
##
## $`2`
## v.test Mean in category Overall mean sd in category
## redIntWthChina 8.381118 3.140625 2.175227 1.5498708
## ctiusAsian 6.807711 2.062500 1.510574 1.4017288
## stpCovStpImmi 6.714696 3.062500 2.291541 1.5799031
## covNgeenLab 5.274671 2.796875 2.203927 1.3883408
## medOrgRecBstInt 5.005710 2.726562 2.217523 1.2100803
## medOrgUntrust 4.656404 2.750000 2.222054 1.4142136
## covNat 4.631311 3.085938 2.569486 1.1860343
## chnsCovRcst 4.521538 2.992188 2.377644 1.8477017
## skeptInfoDocSci 4.146105 2.304688 1.906344 1.1219305
## covPlnnd 3.897222 2.132812 1.773414 1.1065931
## frGovUseCovMndtVacc 2.834352 2.367188 2.045317 1.3102427
## polBgDlIntrst 2.793313 3.687500 3.288520 1.4724024
## scntFkNwsCov 2.534026 2.328125 2.054381 1.1395543
## mdiaUseCovMkTrmpRepLkBd 2.284415 2.687500 2.413897 1.3448397
## fllwRecMedOrgImp 2.133229 2.093750 1.903323 0.8964993
## eldrNoBgDl -2.376438 1.515625 1.712991 0.8290090
## polDwnplCovPlpLDngr -2.694150 1.593750 1.853474 0.8609361
## covNtSerPolSay -3.117003 1.734375 2.074018 0.7340425
## rareNoWorr -3.811344 1.320312 1.623867 0.6365630
## flu -4.051239 1.726562 2.214502 1.2482165
## bgThrt -4.221150 1.984375 2.474320 1.1724998
## afrDie -7.361134 3.046875 3.950151 1.2677451
## Overall sd p.value
## redIntWthChina 1.449906 5.242736e-17
## ctiusAsian 1.020504 9.916397e-12
## stpCovStpImmi 1.445240 1.884582e-11
## covNgeenLab 1.414998 1.329941e-07
## medOrgRecBstInt 1.280033 5.565647e-07
## medOrgUntrust 1.427163 3.217801e-06
## covNat 1.403655 3.633578e-06
## chnsCovRcst 1.710810 6.139184e-06
## skeptInfoDocSci 1.209350 3.381785e-05
## covPlnnd 1.160797 9.730260e-05
## frGovUseCovMndtVacc 1.429428 4.591874e-03
## polBgDlIntrst 1.797906 5.217119e-03
## scntFkNwsCov 1.359782 1.127605e-02
## mdiaUseCovMkTrmpRepLkBd 1.507580 2.234712e-02
## fllwRecMedOrgImp 1.123636 3.290593e-02
## eldrNoBgDl 1.045396 1.748069e-02
## polDwnplCovPlpLDngr 1.213461 7.056832e-03
## covNtSerPolSay 1.371579 1.826999e-03
## rareNoWorr 1.002522 1.382135e-04
## flu 1.516047 5.094722e-05
## bgThrt 1.461005 2.430590e-05
## afrDie 1.544582 1.823549e-13
##
## $`3`
## v.test Mean in category Overall mean sd in category
## mdiaCovBgrDl 8.966381 3.706294 2.649547 1.1577176
## afrDie 8.561785 4.930070 3.950151 1.0944894
## polBgDlIntrst 8.122493 4.370629 3.288520 1.3045285
## bgThrt 7.051951 3.237762 2.474320 1.5326655
## covNtSerPolSay 6.014732 2.685315 2.074018 1.1909894
## mdiaUseCovMkTrmpRepLkBd 4.933610 2.965035 2.413897 1.3401419
## flu 4.751277 2.748252 2.214502 1.3764682
## rareNoWorr 4.498473 1.958042 1.623867 1.0830811
## polDwnplCovPlpLDngr 4.040501 2.216783 1.853474 1.2010477
## eldrNoBgDl 3.434274 1.979021 1.712991 1.2087590
## covNgeenLab -2.278409 1.965035 2.203927 1.1968057
## covNat -2.584242 2.300699 2.569486 1.1468105
## skeptInfoDocSci -3.012769 1.636364 1.906344 0.8239887
## thnksNoCovVacc -3.070873 1.223776 1.451662 0.5344048
## covPlnnd -3.544550 1.468531 1.773414 0.7174566
## redIntWthChina -3.974181 1.748252 2.175227 1.0273772
## Overall sd p.value
## mdiaCovBgrDl 1.590519 3.064164e-19
## afrDie 1.544582 1.111335e-17
## polBgDlIntrst 1.797906 4.567026e-16
## bgThrt 1.461005 1.764265e-12
## covNtSerPolSay 1.371579 1.801847e-09
## mdiaUseCovMkTrmpRepLkBd 1.507580 8.072373e-07
## flu 1.516047 2.021360e-06
## rareNoWorr 1.002522 6.844331e-06
## polDwnplCovPlpLDngr 1.213461 5.333711e-05
## eldrNoBgDl 1.045396 5.941423e-04
## covNgeenLab 1.414998 2.270221e-02
## covNat 1.403655 9.759336e-03
## skeptInfoDocSci 1.209350 2.588758e-03
## thnksNoCovVacc 1.001474 2.134339e-03
## covPlnnd 1.160797 3.932841e-04
## redIntWthChina 1.449906 7.062187e-05
##
## $`4`
## v.test Mean in category Overall mean sd in category
## covNtSerPolSay 16.116064 4.000000 2.074018 1.328020
## thnksNoCovVacc 16.077167 2.854545 1.451662 1.494453
## covPlnnd 15.992419 3.390909 1.773414 1.301017
## fllwRecMedOrgImp 15.751554 3.445455 1.903323 1.156691
## skeptInfoDocSci 15.728036 3.563636 1.906344 1.156226
## frGovUseCovMndtVacc 15.329330 3.954545 2.045317 1.479474
## covVacEffRedVirus 14.930891 3.527273 1.962236 1.405891
## scntFkNwsCov 14.733566 3.800000 2.054381 1.312873
## mdiaCovBgrDl 14.205414 4.618182 2.649547 1.198346
## covNgeenLab 13.535548 3.872727 2.203927 1.321907
## medOrgUntrust 13.493723 3.900000 2.222054 1.313912
## rareNoWorr 13.256351 2.781818 1.623867 1.238634
## mdiaUseCovMkTrmpRepLkBd 13.043666 4.127273 2.413897 1.251247
## polDwnplCovPlpLDngr 12.993456 3.227273 1.853474 1.392572
## medOrgRecBstInt 12.395510 3.600000 2.217523 1.207552
## covNat 11.250597 3.945455 2.569486 1.263734
## eldrNoBgDl 10.636362 2.681818 1.712991 1.220520
## polBgDlIntrst 9.880700 4.836364 3.288520 1.074728
## chnsCovRcst 9.297939 3.763636 2.377644 1.747347
## redIntWthChina 9.263141 3.345455 2.175227 1.592233
## flu 8.561686 3.345455 2.214502 1.404126
## bgThrt 7.414536 3.418182 2.474320 1.448396
## stpCovStpImmi 7.214278 3.200000 2.291541 1.469694
## nwsGdJbComCov 6.483177 3.945455 3.235650 1.241966
## afrDie 5.301536 4.663636 3.950151 1.390077
## ctiusAsian 5.095311 1.963636 1.510574 1.264388
## Overall sd p.value
## covNtSerPolSay 1.371579 1.967484e-58
## thnksNoCovVacc 1.001474 3.688685e-58
## covPlnnd 1.160797 1.443167e-57
## fllwRecMedOrgImp 1.123636 6.701450e-56
## skeptInfoDocSci 1.209350 9.717851e-56
## frGovUseCovMndtVacc 1.429428 4.869804e-53
## covVacEffRedVirus 1.203002 2.074740e-50
## scntFkNwsCov 1.359782 3.924419e-49
## mdiaCovBgrDl 1.590519 8.480047e-46
## covNgeenLab 1.414998 9.646033e-42
## medOrgUntrust 1.427163 1.702793e-41
## rareNoWorr 1.002522 4.145840e-40
## mdiaUseCovMkTrmpRepLkBd 1.507580 6.905503e-39
## polDwnplCovPlpLDngr 1.213461 1.332708e-38
## medOrgRecBstInt 1.280033 2.763830e-35
## covNat 1.403655 2.300306e-29
## eldrNoBgDl 1.045396 2.018568e-26
## polBgDlIntrst 1.797906 5.047906e-23
## chnsCovRcst 1.710810 1.431948e-20
## redIntWthChina 1.449906 1.985045e-20
## flu 1.516047 1.112287e-17
## bgThrt 1.461005 1.220509e-13
## stpCovStpImmi 1.445240 5.422100e-13
## nwsGdJbComCov 1.256547 8.981131e-11
## afrDie 1.544582 1.148326e-07
## ctiusAsian 1.020504 3.481697e-07save(res.mfa, res.hcpc, file='./data/mfa_and_classification.rdata')Analysis
df_covmis$cov_class <- res.hcpc$data.clust$clust
freq(df_covmis$cov_class)## n % val%
## 1 281 42.4 42.4
## 2 128 19.3 19.3
## 3 143 21.6 21.6
## 4 110 16.6 16.6df_covmis %>%
gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
ggplot(., aes(y=Covmis_res, x= cov_class, fill=cov_class)) +
geom_boxplot() +
facet_wrap(~ Covmis_var, ncol = 5)
df_covmis %>%
gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
mutate(Covmis_var=str_remove(Covmis_var,"^covmis_")) %>%
ggplot(., aes(y=Covmis_res, x= Covmis_var, fill=Covmis_var)) +
geom_boxplot() +
facet_wrap(~ cov_class, ncol = 1)
table(df_covmis$EXPGRP_TEXT, df_covmis$cov_class) %>% cprop##
## 1 2 3 4 All
## Chinese 39.5 41.4 30.8 15.5 34.0
## Non-Chinese Asian 1.4 3.1 2.1 3.6 2.3
## White 59.1 55.5 67.1 80.9 63.7
## Total 100.0 100.0 100.0 100.0 100.0table(df_covmis$EXPGRP_TEXT, df_covmis$cov_class) %>% chisq.test()## Warning in chisq.test(.): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: .
## X-squared = 26.043, df = 6, p-value = 0.0002185table(df_covmis$CONTINENT_BORN_TEXT_3, df_covmis$cov_class) %>% lprop##
## 1 2 3 4 Total
## Northern Country (Richer) 30.5 26.2 27.7 15.6 100.0
## Southern Country (Poorer) 32.6 17.4 28.5 21.5 100.0
## USA 50.8 17.5 16.7 15.1 100.0
## All 42.5 19.3 21.6 16.6 100.0table(df_covmis$HAS_LIVED_USA, df_covmis$cov_class) %>% lprop##
## 1 2 3 4 Total
## FALSE 28.4 21.8 29.6 20.2 100.0
## TRUE 50.6 17.9 16.9 14.6 100.0
## All 42.4 19.3 21.6 16.6 100.0By considering the data has quatitative and not qualitative we tend to put closer the outsider that might have answered with extreme response in misconsception data and those who answered in the middle. Let’s try to see if we get different result when analysing qualitative data.
MFA and HCPC with qualitative data
res_mfa_quali <- df_covmis %>%
dplyr::select(matches("^covmis_")) %>%
transmute_all(~fct_recode(.x %>% as_factor,
"-"="1",
"-"="2",
"="="3",
"="="4",
"+"="5",
"+"="6")) %>%
rename_with(function(x){str_remove(x,"^covmis_[A-z]{3,4}_")}) %>%
MFA(.,
group = c(5,4,4,3,3,3,4),
type=c("n","n","n","n","n","n","n"),
name.group = c("att","conspiracy","origin","politics","coverage","antivaccin","Mdsk"))## Warning: ggrepel: 75 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 650 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 631 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_screeplot(res_mfa_quali)
fviz_mfa_var(res_mfa_quali, "quali.var", palette = "jco",
col.var.sup = "violet", repel = TRUE)## Warning: ggrepel: 46 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
quali.var <- get_mfa_var(res_mfa_quali, "quali.var")
quali.var$coord## Dim.1 Dim.2 Dim.3 Dim.4
## flu_- -0.63190250 0.050833492 0.220603615 -0.102234292
## flu_= 1.91188535 -0.686324554 -0.653615784 0.318664496
## flu_+ 0.77387816 0.811082397 -0.292871148 0.109879710
## afrDie_- -0.70772318 0.362624553 0.597935863 0.144596989
## afrDie_= -0.36586938 -0.269474945 0.275542370 -0.224066614
## afrDie_+ 0.65293633 0.099375680 -0.519767947 0.150366717
## eldrNoBgDl_- -0.47282014 0.032429636 0.108111291 -0.054574598
## eldrNoBgDl_= 2.14581844 -0.375682940 -0.434485524 -0.044356928
## eldrNoBgDl_+ 2.36846186 1.107031792 -0.853555003 1.895793010
## rareNoWorr_- -0.52996521 0.043329181 0.147796910 -0.080355204
## rareNoWorr_= 2.64341551 -0.423066246 -0.741149043 0.268203975
## rareNoWorr_+ 3.44950345 1.092676099 -0.935744589 1.403869178
## bgThrt_- -0.65636413 0.079367932 0.316227105 -0.022161560
## bgThrt_= 0.72044850 -0.465718497 -0.466751101 0.083667421
## bgThrt_+ 1.50511906 0.717178561 -0.440978948 -0.090118483
## ctiusAsian_- -0.21275763 0.083061295 -0.153507652 -0.043151998
## ctiusAsian_= 1.34381146 -0.783297671 0.661139793 -0.169574038
## ctiusAsian_+ 1.49787178 0.691327033 2.602369312 2.484971570
## stpCovStpImmi_- -0.54576531 0.122238483 -0.257615393 -0.041449448
## stpCovStpImmi_= 0.74868432 -0.556876503 0.093539163 -0.231879835
## stpCovStpImmi_+ 1.51407766 0.981345121 1.596349329 1.094645337
## redIntWthChina_- -0.60736923 0.104287438 -0.305395915 0.007417633
## redIntWthChina_= 0.93359456 -0.665544000 0.245126346 -0.438047733
## redIntWthChina_+ 2.10827386 1.144543293 1.728903079 1.246433032
## chnsCovRcst_- -0.72490561 0.109846442 -0.161088716 -0.067198938
## chnsCovRcst_= 0.97892904 -0.911042459 0.096985230 -0.212512708
## chnsCovRcst_+ 1.76094828 0.646964797 0.535754103 0.526579793
## covPlnnd_- -0.63762873 -0.016471134 -0.142553390 0.104135949
## covPlnnd_= 2.15719286 -0.471287584 0.478610611 -0.289407343
## covPlnnd_+ 3.94956516 2.420876866 0.899138777 -0.921793491
## covNat_- -0.96434563 0.108142267 -0.188720037 0.137253401
## covNat_= 0.86059857 -0.526803498 0.078175080 -0.046085090
## covNat_+ 1.93256999 1.030034090 0.639669297 -0.496429326
## covNgeenLab_- -0.86166156 0.070903889 -0.154662695 0.118085697
## covNgeenLab_= 1.21152106 -0.711187678 0.132702439 -0.060550725
## covNgeenLab_+ 2.88978652 1.607419243 0.774458373 -0.714322489
## scntFkNwsCov_- -0.87380568 0.097393958 -0.093478240 0.074819444
## scntFkNwsCov_= 1.64927766 -0.853163231 0.293735550 -0.058287603
## scntFkNwsCov_+ 3.10706999 2.251346850 -0.122841408 -0.587895395
## polBgDlIntrst_- -1.44709232 0.289405556 0.090855062 -0.029891094
## polBgDlIntrst_= 0.55758661 -0.803900183 0.015890057 -0.158183705
## polBgDlIntrst_+ 1.30928392 0.536782132 -0.140910440 0.222784533
## covNtSerPolSay_- -0.89138541 0.122403585 0.176116056 -0.067608424
## covNtSerPolSay_= 1.79141834 -1.083564852 -0.247786612 0.050942791
## covNtSerPolSay_+ 3.71343664 2.057411763 -1.059069877 0.541979733
## polDwnplCovPlpLDngr_- -0.69839089 0.067477712 0.162500932 -0.061615364
## polDwnplCovPlpLDngr_= 2.14943859 -1.001469181 -0.416200359 0.082822329
## polDwnplCovPlpLDngr_+ 3.01604711 2.463521473 -0.993053909 0.636788254
## mdiaCovBgrDl_- -1.23885054 0.259024994 0.167471971 -0.079685559
## mdiaCovBgrDl_= 0.91602909 -1.001106249 0.017913283 -0.123011516
## mdiaCovBgrDl_+ 2.63056351 1.239583047 -0.668939821 0.571370135
## nwsGdJbComCov_- -0.72493714 0.151374120 0.284983197 0.097067952
## nwsGdJbComCov_= 0.08717035 -0.348368210 -0.218571211 -0.051326630
## nwsGdJbComCov_+ 0.89886069 0.878774855 0.241960919 0.007732009
## mdiaUseCovMkTrmpRepLkBd_- -1.12248341 0.159968135 0.049544190 -0.091085464
## mdiaUseCovMkTrmpRepLkBd_= 1.22914967 -0.735295879 -0.175622148 -0.037433153
## mdiaUseCovMkTrmpRepLkBd_+ 2.45419741 1.291179539 0.247239184 0.601011671
## frGovUseCovMndtVacc_- -0.84590295 0.094174426 -0.079025286 0.064571950
## frGovUseCovMndtVacc_= 1.70326864 -1.066921952 0.305658054 0.087002171
## frGovUseCovMndtVacc_+ 3.11500872 1.556494194 -0.026902032 -0.708609392
## thnksNoCovVacc_- -0.46380830 0.004210011 -0.008641680 0.090278369
## thnksNoCovVacc_= 3.06075464 -0.943742616 0.214828999 -0.199166483
## thnksNoCovVacc_+ 4.18743783 3.740325403 -0.572908736 -2.451029121
## covVacEffRedVirus_- -0.74228722 0.077724378 -0.004548361 0.093934800
## covVacEffRedVirus_= 1.69244885 -0.857500513 0.095435578 0.023925275
## covVacEffRedVirus_+ 3.87793482 2.732034741 -0.368635083 -1.589080538
## medOrgUntrust_- -0.89597500 0.110754939 -0.176203620 -0.005278715
## medOrgUntrust_= 1.45046553 -0.856373659 0.291036767 0.174068186
## medOrgUntrust_+ 2.51175409 1.708861818 0.476707442 -0.478865800
## skeptInfoDocSci_- -0.75334424 0.032219501 -0.144879549 0.048115568
## skeptInfoDocSci_= 2.05362277 -0.682955092 0.530624780 0.039510823
## skeptInfoDocSci_+ 3.74536243 2.952774686 0.010569411 -1.131971388
## medOrgRecBstInt_- -0.93985508 0.183772105 -0.181016183 0.054420631
## medOrgRecBstInt_= 1.46891595 -0.744657684 0.309630419 0.028337992
## medOrgRecBstInt_+ 2.57089140 1.784492712 0.361568640 -0.715827621
## fllwRecMedOrgImp_- -0.79417647 0.071625960 -0.081299337 0.063930777
## fllwRecMedOrgImp_= 2.02121478 -0.653157916 0.231651854 -0.050889419
## fllwRecMedOrgImp_+ 3.95532158 3.245403330 0.215632118 -1.173803452
## Dim.5
## flu_- -0.0079655558
## flu_= 0.0461187789
## flu_+ -0.0263545589
## afrDie_- 0.1221272809
## afrDie_= 0.2084621420
## afrDie_+ -0.2506808604
## eldrNoBgDl_- 0.0134360912
## eldrNoBgDl_= -0.1512401298
## eldrNoBgDl_+ 0.4341544094
## rareNoWorr_- -0.0466447617
## rareNoWorr_= 0.2228263942
## rareNoWorr_+ 0.3689277201
## bgThrt_- -0.0005361463
## bgThrt_= -0.0182044801
## bgThrt_+ 0.0458627573
## ctiusAsian_- -0.0658698155
## ctiusAsian_= 0.7766442667
## ctiusAsian_+ -1.3152180963
## stpCovStpImmi_- -0.1044003574
## stpCovStpImmi_= 0.3415882043
## stpCovStpImmi_+ -0.3834144668
## redIntWthChina_- -0.1087472639
## redIntWthChina_= 0.3672394473
## redIntWthChina_+ -0.2191314814
## chnsCovRcst_- -0.0037260705
## chnsCovRcst_= 0.2755342662
## chnsCovRcst_+ -0.3150412035
## covPlnnd_- -0.0366525791
## covPlnnd_= 0.4177622784
## covPlnnd_+ -1.0655176572
## covNat_- -0.0834695759
## covNat_= 0.2167123599
## covNat_+ -0.2448062201
## covNgeenLab_- -0.0171709775
## covNgeenLab_= 0.2444473914
## covNgeenLab_+ -0.6071915454
## scntFkNwsCov_- 0.0050214802
## scntFkNwsCov_= 0.0963669372
## scntFkNwsCov_+ -0.4286339810
## polBgDlIntrst_- 0.1908127988
## polBgDlIntrst_= -0.1403244275
## polBgDlIntrst_+ -0.0955901508
## covNtSerPolSay_- 0.1099522485
## covNtSerPolSay_= -0.6609065293
## covNtSerPolSay_+ 0.8904457090
## polDwnplCovPlpLDngr_- 0.0079294445
## polDwnplCovPlpLDngr_= -0.4060573030
## polDwnplCovPlpLDngr_+ 1.2903160314
## mdiaCovBgrDl_- 0.2139165072
## mdiaCovBgrDl_= -0.5275562955
## mdiaCovBgrDl_+ 0.3623013579
## nwsGdJbComCov_- -0.4680839584
## nwsGdJbComCov_= 0.0980314926
## nwsGdJbComCov_+ 0.4455394121
## mdiaUseCovMkTrmpRepLkBd_- 0.1631389586
## mdiaUseCovMkTrmpRepLkBd_= -0.2850575237
## mdiaUseCovMkTrmpRepLkBd_+ -0.0449331861
## frGovUseCovMndtVacc_- 0.0313865589
## frGovUseCovMndtVacc_= -0.0055281030
## frGovUseCovMndtVacc_+ -0.2406952256
## thnksNoCovVacc_- -0.0130021570
## thnksNoCovVacc_= 0.2337653673
## thnksNoCovVacc_+ -0.4929539504
## covVacEffRedVirus_- -0.0299962709
## covVacEffRedVirus_= 0.1917538015
## covVacEffRedVirus_+ -0.4123423681
## medOrgUntrust_- -0.0788470553
## medOrgUntrust_= 0.2772670746
## medOrgUntrust_+ -0.2252093623
## skeptInfoDocSci_- -0.0366734303
## skeptInfoDocSci_= 0.2132988371
## skeptInfoDocSci_+ -0.4104587195
## medOrgRecBstInt_- -0.1184202885
## medOrgRecBstInt_= 0.1906834328
## medOrgRecBstInt_+ 0.2959168797
## fllwRecMedOrgImp_- -0.1048376559
## fllwRecMedOrgImp_= 0.3899442294
## fllwRecMedOrgImp_+ -0.4197926697quali.var$contrib## Dim.1 Dim.2 Dim.3 Dim.4
## flu_- 0.77386479 0.0213882301 1.354803e+00 0.4541934509
## flu_= 1.87791426 1.0335254067 3.152703e+00 1.1697745645
## flu_+ 0.18760888 0.8801327619 3.859645e-01 0.0848058823
## afrDie_- 0.25104701 0.2814832977 2.574088e+00 0.2349792164
## afrDie_= 0.14760566 0.3419776181 1.202579e+00 1.2413319488
## afrDie_+ 0.49503216 0.0489735965 4.506057e+00 0.5886781766
## eldrNoBgDl_- 0.50796969 0.0102056344 3.814818e-01 0.1517433601
## eldrNoBgDl_= 1.92323744 0.2517676462 1.132618e+00 0.0184269145
## eldrNoBgDl_+ 0.42174725 0.3935044645 7.868073e-01 6.0587641071
## rareNoWorr_- 0.65108046 0.0185870693 7.273720e-01 0.3356222167
## rareNoWorr_= 2.71431830 0.2969317757 3.064975e+00 0.6265317333
## rareNoWorr_+ 0.69580599 0.2981727115 7.354879e-01 2.5841101225
## bgThrt_- 0.70537929 0.0440487079 2.351891e+00 0.0180308587
## bgThrt_= 0.41191389 0.7351188295 2.483460e+00 0.1245650112
## bgThrt_+ 0.75696998 0.7340092885 9.333802e-01 0.0608481585
## ctiusAsian_- 0.11738204 0.0764079877 8.777631e-01 0.1082719376
## ctiusAsian_= 0.60476288 0.8775505294 2.102718e+00 0.2159284746
## ctiusAsian_+ 0.15230609 0.1385622988 6.603767e+00 9.3992582222
## stpCovStpImmi_- 0.56076631 0.1201423085 1.794732e+00 0.0725254285
## stpCovStpImmi_= 0.48197865 1.1388279823 1.080695e-01 1.0366664364
## stpCovStpImmi_+ 0.58097983 1.0423613615 9.276978e+00 6.8091729155
## redIntWthChina_- 0.73958148 0.0931224648 2.685918e+00 0.0024733973
## redIntWthChina_= 0.64690007 1.4040550476 6.405993e-01 3.1933476020
## redIntWthChina_+ 1.10635236 1.3925598858 1.068727e+01 8.6708178338
## chnsCovRcst_- 1.02022729 0.1000497544 7.236860e-01 0.1965810174
## chnsCovRcst_= 0.55078597 2.0373596398 7.765646e-02 0.5820134271
## chnsCovRcst_+ 1.48756446 0.8575390541 1.977878e+00 2.9825911005
## covPlnnd_- 0.76480252 0.0021795686 5.491039e-01 0.4574015686
## covPlnnd_= 1.82714732 0.3724582757 1.291951e+00 0.7373903596
## covPlnnd_+ 1.39200728 2.2335637684 1.036292e+00 1.7001723963
## covNat_- 1.18836922 0.0638244251 6.537443e-01 0.5397776975
## covNat_= 0.59746520 0.9561338333 7.081642e-02 0.0384162522
## covNat_+ 1.03984354 1.2615682377 1.636419e+00 1.5384916311
## covNgeenLab_- 1.14753057 0.0331849336 5.310653e-01 0.4832458863
## covNgeenLab_= 0.90114217 1.3262024961 1.553013e-01 0.0504722681
## covNgeenLab_+ 1.69906233 2.2451539647 1.752911e+00 2.3278188488
## scntFkNwsCov_- 1.24551472 0.0660835220 2.047510e-01 0.2047532626
## scntFkNwsCov_= 1.58262525 1.8086928089 7.210900e-01 0.0443226455
## scntFkNwsCov_+ 1.44728578 3.2452417561 3.249584e-02 1.1618099330
## polBgDlIntrst_- 2.69343582 0.4600848105 1.525100e-01 0.0257679885
## polBgDlIntrst_= 0.34186539 3.0349024332 3.988111e-03 0.6169311461
## polBgDlIntrst_+ 1.63419338 1.1731185224 2.718993e-01 1.0609345466
## covNtSerPolSay_- 1.90370051 0.1533083721 1.067459e+00 0.2455567904
## covNtSerPolSay_= 2.28237559 3.5662592907 6.272413e-01 0.0413848425
## covNtSerPolSay_+ 3.19951270 4.1945391659 3.738238e+00 1.5282015688
## polDwnplCovPlpLDngr_- 1.25470400 0.0500235906 9.757572e-01 0.2189802387
## polDwnplCovPlpLDngr_= 2.74983137 2.5494187194 1.480972e+00 0.0915447836
## polDwnplCovPlpLDngr_+ 1.56001628 4.4450393160 2.429318e+00 1.5592857912
## mdiaCovBgrDl_- 3.20209385 0.5978463550 8.405555e-01 0.2970559257
## mdiaCovBgrDl_= 1.02983071 5.2531241524 5.656966e-03 0.4164104221
## mdiaCovBgrDl_+ 3.84192781 3.6434506161 3.568713e+00 4.0641472569
## nwsGdJbComCov_- 0.56512762 0.1052347355 1.254499e+00 0.2271853433
## nwsGdJbComCov_= 0.01620908 1.1056251911 1.463837e+00 0.1260053918
## nwsGdJbComCov_+ 0.53356978 2.1780693964 5.553705e-01 0.0008852662
## mdiaUseCovMkTrmpRepLkBd_- 2.82024379 0.2446266466 7.892201e-02 0.4163970892
## mdiaUseCovMkTrmpRepLkBd_= 1.83654208 2.8068914986 5.385621e-01 0.0381932756
## mdiaUseCovMkTrmpRepLkBd_+ 2.49922496 2.9544023846 3.643400e-01 3.3607400174
## frGovUseCovMndtVacc_- 1.48465769 0.0785889045 1.861244e-01 0.1939794857
## frGovUseCovMndtVacc_= 1.62206374 2.7181719815 7.503419e-01 0.0948952649
## frGovUseCovMndtVacc_+ 2.50070344 2.6665435544 2.679167e-03 2.9016178310
## thnksNoCovVacc_- 0.54500020 0.0001917768 2.717702e-03 0.4629880591
## thnksNoCovVacc_= 2.70080118 1.0966113304 1.911205e-01 0.2564193747
## thnksNoCovVacc_+ 1.22548606 4.1758069845 3.295099e-01 9.4143711559
## covVacEffRedVirus_- 1.17450653 0.0549966189 6.334425e-04 0.4217423791
## covVacEffRedVirus_= 1.78919957 1.9615820306 8.172110e-02 0.0080172290
## covVacEffRedVirus_+ 2.03635902 4.3165401903 2.643216e-01 7.6670430626
## medOrgUntrust_- 1.17977515 0.0769916599 6.554250e-01 0.0009182198
## medOrgUntrust_= 1.20831998 1.7988864244 6.987933e-01 0.3902012715
## medOrgUntrust_+ 1.21491893 2.4016901108 6.286122e-01 0.9901556060
## skeptInfoDocSci_- 0.95868328 0.0074892073 5.093171e-01 0.0876884107
## skeptInfoDocSci_= 1.93775317 0.9152746300 1.858310e+00 0.0160831831
## skeptInfoDocSci_+ 1.23219447 3.2708617478 1.409545e-04 2.5237434230
## medOrgRecBstInt_- 1.25936740 0.2056363337 6.710446e-01 0.0946761423
## medOrgRecBstInt_= 1.45794820 1.6001900576 9.305107e-01 0.0121665965
## medOrgRecBstInt_+ 0.89319372 1.8378826172 2.537731e-01 1.5526653042
## fllwRecMedOrgImp_- 1.04198411 0.0361974516 1.568509e-01 0.1514013622
## fllwRecMedOrgImp_= 2.11171142 0.9417942663 3.984438e-01 0.0300156235
## fllwRecMedOrgImp_+ 1.05708960 3.0394539810 4.512955e-02 2.0874770661
## Dim.5
## flu_- 3.333352e-03
## flu_= 2.962045e-02
## flu_+ 5.897986e-03
## afrDie_- 2.026458e-01
## afrDie_= 1.298941e+00
## afrDie_+ 1.977963e+00
## eldrNoBgDl_- 1.111926e-02
## eldrNoBgDl_= 2.589793e-01
## eldrNoBgDl_+ 3.841421e-01
## rareNoWorr_- 1.367193e-01
## rareNoWorr_= 5.228140e-01
## rareNoWorr_+ 2.157454e-01
## bgThrt_- 1.275803e-05
## bgThrt_= 7.129206e-03
## bgThrt_+ 1.905203e-02
## ctiusAsian_- 3.049919e-01
## ctiusAsian_= 5.475664e+00
## ctiusAsian_+ 3.183080e+00
## stpCovStpImmi_- 5.562347e-01
## stpCovStpImmi_= 2.719694e+00
## stpCovStpImmi_+ 1.009917e+00
## redIntWthChina_- 6.426895e-01
## redIntWthChina_= 2.713334e+00
## redIntWthChina_+ 3.239911e-01
## chnsCovRcst_- 7.306682e-04
## chnsCovRcst_= 1.182812e+00
## chnsCovRcst_+ 1.290629e+00
## covPlnnd_- 6.850254e-02
## covPlnnd_= 1.857540e+00
## covPlnnd_+ 2.746302e+00
## covNat_- 2.413384e-01
## covNat_= 1.026982e+00
## covNat_+ 4.522999e-01
## covNgeenLab_- 1.235279e-02
## covNgeenLab_= 9.944575e-01
## covNgeenLab_+ 2.033355e+00
## scntFkNwsCov_- 1.114979e-03
## scntFkNwsCov_= 1.464639e-01
## scntFkNwsCov_+ 7.466369e-01
## polBgDlIntrst_- 1.269443e+00
## polBgDlIntrst_= 5.869229e-01
## polBgDlIntrst_+ 2.361273e-01
## covNtSerPolSay_- 7.851631e-01
## covNtSerPolSay_= 8.420875e+00
## covNtSerPolSay_+ 4.986901e+00
## polDwnplCovPlpLDngr_- 4.384436e-03
## polDwnplCovPlpLDngr_= 2.660202e+00
## polDwnplCovPlpLDngr_+ 7.739789e+00
## mdiaCovBgrDl_- 2.588027e+00
## mdiaCovBgrDl_= 9.259104e+00
## mdiaCovBgrDl_+ 1.975493e+00
## nwsGdJbComCov_- 6.386713e+00
## nwsGdJbComCov_= 5.556946e-01
## nwsGdJbComCov_+ 3.553553e+00
## mdiaUseCovMkTrmpRepLkBd_- 1.614828e+00
## mdiaUseCovMkTrmpRepLkBd_= 2.677567e+00
## mdiaUseCovMkTrmpRepLkBd_+ 2.270936e-02
## frGovUseCovMndtVacc_- 5.540599e-02
## frGovUseCovMndtVacc_= 4.631673e-04
## frGovUseCovMndtVacc_+ 4.047274e-01
## thnksNoCovVacc_- 1.161006e-02
## thnksNoCovVacc_= 4.270512e-01
## thnksNoCovVacc_+ 4.603714e-01
## covVacEffRedVirus_- 5.199123e-02
## covVacEffRedVirus_= 6.225865e-01
## covVacEffRedVirus_+ 6.240987e-01
## medOrgUntrust_- 2.476638e-01
## medOrgUntrust_= 1.196872e+00
## medOrgUntrust_+ 2.647586e-01
## skeptInfoDocSci_- 6.158505e-02
## skeptInfoDocSci_= 5.666542e-01
## skeptInfoDocSci_+ 4.011572e-01
## medOrgRecBstInt_- 5.419596e-01
## medOrgRecBstInt_= 6.659754e-01
## medOrgRecBstInt_+ 3.207759e-01
## fllwRecMedOrgImp_- 4.922047e-01
## fllwRecMedOrgImp_= 2.130584e+00
## fllwRecMedOrgImp_+ 3.227765e-01Cassification
res.hcpc <- HCPC(res_mfa_quali, nb.clust=3, graph = T)
fviz_cluster(res.hcpc,
repel = TRUE,
show.clust.cent = TRUE,
palette = "jco",
ggtheme = theme_minimal(),
main = "Factor map"
)## Warning: ggrepel: 597 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
res.hcpc$desc.var$category## $`1`
## Cla/Mod Mod/Cla
## covNtSerPolSay=covNtSerPolSay_- 88.000000 93.0957684
## mdiaCovBgrDl=mdiaCovBgrDl_- 94.117647 74.8329621
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 92.167102 78.6191537
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 85.684211 90.6458797
## scntFkNwsCov=scntFkNwsCov_- 86.433260 87.9732739
## skeptInfoDocSci=skeptInfoDocSci_- 83.400000 92.8730512
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 83.844581 91.3140312
## medOrgRecBstInt=medOrgRecBstInt_- 87.440758 82.1826281
## covVacEffRedVirus=covVacEffRedVirus_- 83.606557 90.8685969
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 82.156863 93.3184855
## medOrgUntrust=medOrgUntrust_- 86.206897 83.5189310
## polBgDlIntrst=polBgDlIntrst_- 96.078431 54.5657016
## covNgeenLab=covNgeenLab_- 85.681293 82.6280624
## covPlnnd=covPlnnd_- 80.265655 94.2093541
## thnksNoCovVacc=thnksNoCovVacc_- 76.724138 99.1091314
## rareNoWorr=rareNoWorr_- 78.198198 96.6592428
## covNat=covNat_- 86.592179 69.0423163
## eldrNoBgDl=eldrNoBgDl_- 77.573529 93.9866370
## chnsCovRcst=chnsCovRcst_- 82.284382 78.6191537
## flu=flu_- 80.387931 83.0734967
## redIntWthChina=redIntWthChina_- 78.329571 77.2828508
## stpCovStpImmi=stpCovStpImmi_- 79.086538 73.2739421
## bgThrt=bgThrt_- 79.591837 69.4877506
## ctiusAsian=ctiusAsian_- 72.076789 91.9821826
## afrDie=afrDie_- 82.500000 22.0489978
## nwsGdJbComCov=nwsGdJbComCov_- 78.804348 32.2939866
## afrDie=afrDie_= 75.378788 44.3207127
## nwsGdJbComCov=nwsGdJbComCov_= 64.383562 52.3385301
## eldrNoBgDl=eldrNoBgDl_+ 27.777778 1.1135857
## stpCovStpImmi=stpCovStpImmi_+ 44.642857 5.5679287
## medOrgRecBstInt=medOrgRecBstInt_+ 37.500000 3.3407572
## bgThrt=bgThrt_+ 45.000000 8.0178174
## rareNoWorr=rareNoWorr_+ 7.142857 0.2227171
## bgThrt=bgThrt_= 53.157895 22.4944321
## thnksNoCovVacc=thnksNoCovVacc_+ 6.250000 0.2227171
## skeptInfoDocSci=skeptInfoDocSci_+ 19.230769 1.1135857
## redIntWthChina=redIntWthChina_+ 34.545455 4.2316258
## redIntWthChina=redIntWthChina_= 50.609756 18.4855234
## ctiusAsian=ctiusAsian_= 39.189189 6.4587973
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ 10.000000 0.4454343
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ 23.529412 1.7817372
## medOrgUntrust=medOrgUntrust_+ 33.333333 4.2316258
## polBgDlIntrst=polBgDlIntrst_= 52.752294 25.6124722
## chnsCovRcst=chnsCovRcst_= 44.881890 12.6948775
## stpCovStpImmi=stpCovStpImmi_= 50.000000 21.1581292
## covNat=covNat_+ 35.897436 6.2360802
## covPlnnd=covPlnnd_+ 8.000000 0.4454343
## afrDie=afrDie_+ 54.316547 33.6302895
## covVacEffRedVirus=covVacEffRedVirus_+ 12.903226 0.8908686
## scntFkNwsCov=scntFkNwsCov_+ 16.666667 1.5590200
## polBgDlIntrst=polBgDlIntrst_+ 47.089947 19.8218263
## chnsCovRcst=chnsCovRcst_+ 36.792453 8.6859688
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 28.169014 4.4543430
## covNat=covNat_= 49.115044 24.7216036
## covNgeenLab=covNgeenLab_+ 17.543860 2.2271715
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ 15.254237 2.0044543
## covNgeenLab=covNgeenLab_= 39.534884 15.1447661
## mdiaCovBgrDl=mdiaCovBgrDl_= 42.857143 20.0445434
## covNtSerPolSay=covNtSerPolSay_+ 4.347826 0.4454343
## mdiaCovBgrDl=mdiaCovBgrDl_+ 24.210526 5.1224944
## eldrNoBgDl=eldrNoBgDl_= 22.000000 4.8997773
## flu=flu_= 25.203252 6.9042316
## covPlnnd=covPlnnd_= 21.818182 5.3452116
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_= 25.781250 7.3496659
## medOrgUntrust=medOrgUntrust_= 32.352941 12.2494432
## rareNoWorr=rareNoWorr_= 15.053763 3.1180401
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 36.538462 16.9265033
## thnksNoCovVacc=thnksNoCovVacc_= 4.545455 0.6681514
## covVacEffRedVirus=covVacEffRedVirus_= 25.874126 8.2405345
## scntFkNwsCov=scntFkNwsCov_= 28.834356 10.4677060
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_= 18.644068 4.8997773
## medOrgRecBstInt=medOrgRecBstInt_= 32.500000 14.4766147
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 24.183007 8.2405345
## skeptInfoDocSci=skeptInfoDocSci_= 19.852941 6.0133630
## covNtSerPolSay=covNtSerPolSay_= 20.567376 6.4587973
## Global p.value
## covNtSerPolSay=covNtSerPolSay_- 71.752266 5.117451e-70
## mdiaCovBgrDl=mdiaCovBgrDl_- 53.927492 3.448964e-60
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 57.854985 1.502251e-58
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 71.752266 2.824687e-54
## scntFkNwsCov=scntFkNwsCov_- 69.033233 3.902610e-52
## skeptInfoDocSci=skeptInfoDocSci_- 75.528701 1.150138e-49
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 73.867069 3.083620e-48
## medOrgRecBstInt=medOrgRecBstInt_- 63.746224 1.115046e-46
## covVacEffRedVirus=covVacEffRedVirus_- 73.716012 1.895507e-46
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 77.039275 1.458702e-45
## medOrgUntrust=medOrgUntrust_- 65.709970 2.917191e-44
## polBgDlIntrst=polBgDlIntrst_- 38.519637 2.840440e-41
## covNgeenLab=covNgeenLab_- 65.407855 2.870468e-41
## covPlnnd=covPlnnd_- 79.607251 8.129743e-40
## thnksNoCovVacc=thnksNoCovVacc_- 87.613293 2.492696e-38
## rareNoWorr=rareNoWorr_- 83.836858 6.288206e-37
## covNat=covNat_- 54.078550 5.464027e-30
## eldrNoBgDl=eldrNoBgDl_- 82.175227 6.475358e-29
## chnsCovRcst=chnsCovRcst_- 64.803625 1.034163e-26
## flu=flu_- 70.090634 2.786292e-25
## redIntWthChina=redIntWthChina_- 66.918429 5.561513e-16
## stpCovStpImmi=stpCovStpImmi_- 62.839879 1.412665e-15
## bgThrt=bgThrt_- 59.214502 7.921409e-15
## ctiusAsian=ctiusAsian_- 86.555891 1.151745e-08
## afrDie=afrDie_- 18.126888 8.326943e-05
## nwsGdJbComCov=nwsGdJbComCov_- 27.794562 1.330351e-04
## afrDie=afrDie_= 39.879154 6.586012e-04
## nwsGdJbComCov=nwsGdJbComCov_= 55.135952 3.583937e-02
## eldrNoBgDl=eldrNoBgDl_+ 2.719033 5.640024e-04
## stpCovStpImmi=stpCovStpImmi_+ 8.459215 2.006869e-04
## medOrgRecBstInt=medOrgRecBstInt_+ 6.042296 5.755603e-05
## bgThrt=bgThrt_+ 12.084592 7.360221e-06
## rareNoWorr=rareNoWorr_+ 2.114804 3.161954e-06
## bgThrt=bgThrt_= 28.700906 4.944141e-07
## thnksNoCovVacc=thnksNoCovVacc_+ 2.416918 3.403001e-07
## skeptInfoDocSci=skeptInfoDocSci_+ 3.927492 3.019986e-07
## redIntWthChina=redIntWthChina_+ 8.308157 1.475632e-07
## redIntWthChina=redIntWthChina_= 24.773414 1.082836e-07
## ctiusAsian=ctiusAsian_= 11.178248 8.449483e-08
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ 3.021148 8.314454e-08
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ 5.135952 7.397510e-08
## medOrgUntrust=medOrgUntrust_+ 8.610272 2.764520e-08
## polBgDlIntrst=polBgDlIntrst_= 32.930514 1.019481e-08
## chnsCovRcst=chnsCovRcst_= 19.184290 2.529324e-09
## stpCovStpImmi=stpCovStpImmi_= 28.700906 1.054052e-09
## covNat=covNat_+ 11.782477 7.705449e-10
## covPlnnd=covPlnnd_+ 3.776435 3.208593e-10
## afrDie=afrDie_+ 41.993958 3.033776e-10
## covVacEffRedVirus=covVacEffRedVirus_+ 4.682779 1.361676e-10
## scntFkNwsCov=scntFkNwsCov_+ 6.344411 2.254740e-12
## polBgDlIntrst=polBgDlIntrst_+ 28.549849 1.632458e-12
## chnsCovRcst=chnsCovRcst_+ 16.012085 7.062492e-13
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 10.725076 4.578863e-13
## covNat=covNat_= 34.138973 2.646177e-13
## covNgeenLab=covNgeenLab_+ 8.610272 3.311659e-16
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ 8.912387 2.621952e-18
## covNgeenLab=covNgeenLab_= 25.981873 2.390130e-19
## mdiaCovBgrDl=mdiaCovBgrDl_= 31.722054 3.240580e-20
## covNtSerPolSay=covNtSerPolSay_+ 6.948640 4.383206e-21
## mdiaCovBgrDl=mdiaCovBgrDl_+ 14.350453 3.024052e-21
## eldrNoBgDl=eldrNoBgDl_= 15.105740 9.441374e-25
## flu=flu_= 18.580060 2.017154e-27
## covPlnnd=covPlnnd_= 16.616314 7.537646e-28
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_= 19.335347 4.935292e-28
## medOrgUntrust=medOrgUntrust_= 25.679758 3.999760e-29
## rareNoWorr=rareNoWorr_= 14.048338 3.730395e-30
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 31.419940 1.577483e-30
## thnksNoCovVacc=thnksNoCovVacc_= 9.969789 1.425645e-30
## covVacEffRedVirus=covVacEffRedVirus_= 21.601208 4.638434e-32
## scntFkNwsCov=scntFkNwsCov_= 24.622356 5.102054e-33
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_= 17.824773 1.466528e-34
## medOrgRecBstInt=medOrgRecBstInt_= 30.211480 2.004316e-36
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 23.111782 7.762233e-38
## skeptInfoDocSci=skeptInfoDocSci_= 20.543807 3.302467e-39
## covNtSerPolSay=covNtSerPolSay_= 21.299094 7.599613e-40
## v.test
## covNtSerPolSay=covNtSerPolSay_- 17.688777
## mdiaCovBgrDl=mdiaCovBgrDl_- 16.364134
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 16.132732
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 15.513195
## scntFkNwsCov=scntFkNwsCov_- 15.193540
## skeptInfoDocSci=skeptInfoDocSci_- 14.816259
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 14.593626
## medOrgRecBstInt=medOrgRecBstInt_- 14.346839
## covVacEffRedVirus=covVacEffRedVirus_- 14.309986
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 14.167366
## medOrgUntrust=medOrgUntrust_- 13.955386
## polBgDlIntrst=polBgDlIntrst_- 13.455955
## covNgeenLab=covNgeenLab_- 13.455178
## covPlnnd=covPlnnd_- 13.205739
## thnksNoCovVacc=thnksNoCovVacc_- 12.945459
## rareNoWorr=rareNoWorr_- 12.695184
## covNat=covNat_- 11.376680
## eldrNoBgDl=eldrNoBgDl_- 11.158948
## chnsCovRcst=chnsCovRcst_- 10.698520
## flu=flu_- 10.388872
## redIntWthChina=redIntWthChina_- 8.098557
## stpCovStpImmi=stpCovStpImmi_- 7.984348
## bgThrt=bgThrt_- 7.768830
## ctiusAsian=ctiusAsian_- 5.706719
## afrDie=afrDie_- 3.934790
## nwsGdJbComCov=nwsGdJbComCov_- 3.820771
## afrDie=afrDie_= 3.406257
## nwsGdJbComCov=nwsGdJbComCov_= -2.098745
## eldrNoBgDl=eldrNoBgDl_+ -3.448360
## stpCovStpImmi=stpCovStpImmi_+ -3.718150
## medOrgRecBstInt=medOrgRecBstInt_+ -4.022613
## bgThrt=bgThrt_+ -4.482996
## rareNoWorr=rareNoWorr_+ -4.660009
## bgThrt=bgThrt_= -5.028468
## thnksNoCovVacc=thnksNoCovVacc_+ -5.099640
## skeptInfoDocSci=skeptInfoDocSci_+ -5.122195
## redIntWthChina=redIntWthChina_+ -5.255574
## redIntWthChina=redIntWthChina_= -5.312244
## ctiusAsian=ctiusAsian_= -5.357257
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ -5.360168
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ -5.381239
## medOrgUntrust=medOrgUntrust_+ -5.555731
## polBgDlIntrst=polBgDlIntrst_= -5.727456
## chnsCovRcst=chnsCovRcst_= -5.959551
## stpCovStpImmi=stpCovStpImmi_= -6.101003
## covNat=covNat_+ -6.150879
## covPlnnd=covPlnnd_+ -6.288349
## afrDie=afrDie_+ -6.297043
## covVacEffRedVirus=covVacEffRedVirus_+ -6.420119
## scntFkNwsCov=scntFkNwsCov_+ -7.017747
## polBgDlIntrst=polBgDlIntrst_+ -7.062744
## chnsCovRcst=chnsCovRcst_+ -7.178220
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ -7.237246
## covNat=covNat_= -7.311276
## covNgeenLab=covNgeenLab_+ -8.161399
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ -8.726707
## covNgeenLab=covNgeenLab_= -8.993714
## mdiaCovBgrDl=mdiaCovBgrDl_= -9.210682
## covNtSerPolSay=covNtSerPolSay_+ -9.423014
## mdiaCovBgrDl=mdiaCovBgrDl_+ -9.461897
## eldrNoBgDl=eldrNoBgDl_= -10.271813
## flu=flu_= -10.848956
## covPlnnd=covPlnnd_= -10.938574
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_= -10.976908
## medOrgUntrust=medOrgUntrust_= -11.201702
## rareNoWorr=rareNoWorr_= -11.409928
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= -11.484554
## thnksNoCovVacc=thnksNoCovVacc_= -11.493297
## covVacEffRedVirus=covVacEffRedVirus_= -11.785467
## scntFkNwsCov=scntFkNwsCov_= -11.970013
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_= -12.261014
## medOrgRecBstInt=medOrgRecBstInt_= -12.604106
## fllwRecMedOrgImp=fllwRecMedOrgImp_= -12.857935
## skeptInfoDocSci=skeptInfoDocSci_= -13.099772
## covNtSerPolSay=covNtSerPolSay_= -13.210816
##
## $`2`
## Cla/Mod Mod/Cla Global
## covNtSerPolSay=covNtSerPolSay_= 70.921986 62.111801 21.299094
## medOrgRecBstInt=medOrgRecBstInt_= 58.000000 72.049689 30.211480
## scntFkNwsCov=scntFkNwsCov_= 61.963190 62.732919 24.622356
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_= 70.338983 51.552795 17.824773
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_= 67.187500 53.416149 19.335347
## skeptInfoDocSci=skeptInfoDocSci_= 64.705882 54.658385 20.543807
## mdiaCovBgrDl=mdiaCovBgrDl_= 53.333333 69.565217 31.722054
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 53.365385 68.944099 31.419940
## covVacEffRedVirus=covVacEffRedVirus_= 61.538462 54.658385 21.601208
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 59.477124 56.521739 23.111782
## medOrgUntrust=medOrgUntrust_= 56.470588 59.627329 25.679758
## flu=flu_= 59.349593 45.341615 18.580060
## thnksNoCovVacc=thnksNoCovVacc_= 72.727273 29.813665 9.969789
## covNgeenLab=covNgeenLab_= 50.000000 53.416149 25.981873
## covPlnnd=covPlnnd_= 58.181818 39.751553 16.616314
## polBgDlIntrst=polBgDlIntrst_= 44.495413 60.248447 32.930514
## eldrNoBgDl=eldrNoBgDl_= 59.000000 36.645963 15.105740
## rareNoWorr=rareNoWorr_= 60.215054 34.782609 14.048338
## covNat=covNat_= 42.920354 60.248447 34.138973
## chnsCovRcst=chnsCovRcst_= 49.606299 39.130435 19.184290
## stpCovStpImmi=stpCovStpImmi_= 40.000000 47.204969 28.700906
## redIntWthChina=redIntWthChina_= 40.243902 40.993789 24.773414
## ctiusAsian=ctiusAsian_= 50.000000 22.981366 11.178248
## bgThrt=bgThrt_= 36.842105 43.478261 28.700906
## afrDie=afrDie_+ 32.014388 55.279503 41.993958
## chnsCovRcst=chnsCovRcst_+ 39.622642 26.086957 16.012085
## nwsGdJbComCov=nwsGdJbComCov_= 29.589041 67.080745 55.135952
## covNgeenLab=covNgeenLab_+ 36.842105 13.043478 8.610272
## mdiaCovBgrDl=mdiaCovBgrDl_+ 32.631579 19.254658 14.350453
## thnksNoCovVacc=thnksNoCovVacc_+ 0.000000 0.000000 2.416918
## nwsGdJbComCov=nwsGdJbComCov_- 17.391304 19.875776 27.794562
## afrDie=afrDie_- 13.333333 9.937888 18.126888
## bgThrt=bgThrt_- 17.602041 42.857143 59.214502
## ctiusAsian=ctiusAsian_- 20.767888 73.913043 86.555891
## redIntWthChina=redIntWthChina_- 17.155756 47.204969 66.918429
## stpCovStpImmi=stpCovStpImmi_- 16.105769 41.614907 62.839879
## thnksNoCovVacc=thnksNoCovVacc_- 19.482759 70.186335 87.613293
## flu=flu_- 16.163793 46.583851 70.090634
## eldrNoBgDl=eldrNoBgDl_- 17.830882 60.248447 82.175227
## covPlnnd=covPlnnd_- 17.077799 55.900621 79.607251
## rareNoWorr=rareNoWorr_- 17.837838 61.490683 83.836858
## covNat=covNat_- 11.173184 24.844720 54.078550
## chnsCovRcst=chnsCovRcst_- 13.053613 34.782609 64.803625
## covNgeenLab=covNgeenLab_- 12.471132 33.540373 65.407855
## covVacEffRedVirus=covVacEffRedVirus_- 13.729508 41.614907 73.716012
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 14.509804 45.962733 77.039275
## skeptInfoDocSci=skeptInfoDocSci_- 14.000000 43.478261 75.528701
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 13.496933 40.993789 73.867069
## polBgDlIntrst=polBgDlIntrst_- 3.921569 6.211180 38.519637
## medOrgUntrust=medOrgUntrust_- 11.264368 30.434783 65.709970
## scntFkNwsCov=scntFkNwsCov_- 11.159737 31.677019 69.033233
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 11.578947 34.161491 71.752266
## medOrgRecBstInt=medOrgRecBstInt_- 9.241706 24.223602 63.746224
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 6.788512 16.149068 57.854985
## covNtSerPolSay=covNtSerPolSay_- 10.315789 30.434783 71.752266
## mdiaCovBgrDl=mdiaCovBgrDl_- 5.042017 11.180124 53.927492
## p.value v.test
## covNtSerPolSay=covNtSerPolSay_= 1.643567e-42 13.664977
## medOrgRecBstInt=medOrgRecBstInt_= 5.418494e-38 12.885695
## scntFkNwsCov=scntFkNwsCov_= 1.635926e-34 12.252154
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_= 7.501051e-33 11.937994
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_= 7.507180e-32 11.744832
## skeptInfoDocSci=skeptInfoDocSci_= 8.568002e-31 11.537189
## mdiaCovBgrDl=mdiaCovBgrDl_= 8.768821e-31 11.535195
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 2.240442e-30 11.454191
## covVacEffRedVirus=covVacEffRedVirus_= 3.169614e-28 11.016849
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 8.891498e-28 10.923585
## medOrgUntrust=medOrgUntrust_= 3.441422e-27 10.800016
## flu=flu_= 7.282717e-21 9.369572
## thnksNoCovVacc=thnksNoCovVacc_= 1.526726e-18 8.787686
## covNgeenLab=covNgeenLab_= 2.775084e-18 8.720283
## covPlnnd=covPlnnd_= 3.364411e-17 8.433165
## polBgDlIntrst=polBgDlIntrst_= 1.490256e-16 8.257283
## eldrNoBgDl=eldrNoBgDl_= 4.288605e-16 8.130121
## rareNoWorr=rareNoWorr_= 9.026386e-16 8.039421
## covNat=covNat_= 4.306726e-15 7.845663
## chnsCovRcst=chnsCovRcst_= 3.258738e-12 6.966089
## stpCovStpImmi=stpCovStpImmi_= 7.566876e-09 5.777833
## redIntWthChina=redIntWthChina_= 1.318014e-07 5.276323
## ctiusAsian=ctiusAsian_= 3.719588e-07 5.082776
## bgThrt=bgThrt_= 3.618849e-06 4.632152
## afrDie=afrDie_+ 9.946597e-05 3.891891
## chnsCovRcst=chnsCovRcst_+ 1.308351e-04 3.824881
## nwsGdJbComCov=nwsGdJbComCov_= 4.323362e-04 3.519515
## covNgeenLab=covNgeenLab_+ 2.748697e-02 2.204532
## mdiaCovBgrDl=mdiaCovBgrDl_+ 4.725275e-02 1.984028
## thnksNoCovVacc=thnksNoCovVacc_+ 1.091212e-02 -2.545500
## nwsGdJbComCov=nwsGdJbComCov_- 8.865527e-03 -2.617197
## afrDie=afrDie_- 1.268437e-03 -3.223027
## bgThrt=bgThrt_- 1.552325e-06 -4.804379
## ctiusAsian=ctiusAsian_- 3.997675e-07 -5.069068
## redIntWthChina=redIntWthChina_- 2.439588e-09 -5.965451
## stpCovStpImmi=stpCovStpImmi_- 2.964488e-10 -6.300624
## thnksNoCovVacc=thnksNoCovVacc_- 1.123394e-12 -7.114475
## flu=flu_- 4.282410e-13 -7.246322
## eldrNoBgDl=eldrNoBgDl_- 5.290579e-15 -7.819804
## covPlnnd=covPlnnd_- 6.217674e-16 -8.084975
## rareNoWorr=rareNoWorr_- 1.867630e-16 -8.230289
## covNat=covNat_- 6.217452e-18 -8.628477
## chnsCovRcst=chnsCovRcst_- 2.572096e-19 -8.985650
## covNgeenLab=covNgeenLab_- 1.109366e-21 -9.566173
## covVacEffRedVirus=covVacEffRedVirus_- 3.043118e-24 -10.158308
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 2.287756e-24 -10.186092
## skeptInfoDocSci=skeptInfoDocSci_- 5.459649e-25 -10.324511
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 2.104669e-25 -10.415600
## polBgDlIntrst=polBgDlIntrst_- 4.176342e-26 -10.568383
## medOrgUntrust=medOrgUntrust_- 2.477537e-26 -10.617250
## scntFkNwsCov=scntFkNwsCov_- 2.954075e-30 -11.430206
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 1.183900e-31 -11.706258
## medOrgRecBstInt=medOrgRecBstInt_- 1.867567e-32 -11.861870
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 6.344308e-36 -12.512926
## covNtSerPolSay=covNtSerPolSay_- 5.552352e-38 -12.883813
## mdiaCovBgrDl=mdiaCovBgrDl_- 1.489914e-38 -12.984921
##
## $`3`
## Cla/Mod Mod/Cla
## covNtSerPolSay=covNtSerPolSay_+ 69.5652174 61.538462
## mdiaCovBgrDl=mdiaCovBgrDl_+ 43.1578947 78.846154
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ 50.8474576 57.692308
## scntFkNwsCov=scntFkNwsCov_+ 61.9047619 50.000000
## polBgDlIntrst=polBgDlIntrst_+ 24.3386243 88.461538
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ 64.7058824 42.307692
## covVacEffRedVirus=covVacEffRedVirus_+ 67.7419355 40.384615
## thnksNoCovVacc=thnksNoCovVacc_+ 93.7500000 28.846154
## covNgeenLab=covNgeenLab_+ 45.6140351 50.000000
## skeptInfoDocSci=skeptInfoDocSci_+ 69.2307692 34.615385
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 38.0281690 51.923077
## covPlnnd=covPlnnd_+ 64.0000000 30.769231
## covNat=covNat_+ 33.3333333 50.000000
## medOrgRecBstInt=medOrgRecBstInt_+ 47.5000000 36.538462
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ 70.0000000 26.923077
## medOrgUntrust=medOrgUntrust_+ 38.5964912 42.307692
## bgThrt=bgThrt_+ 27.5000000 42.307692
## chnsCovRcst=chnsCovRcst_+ 23.5849057 48.076923
## rareNoWorr=rareNoWorr_= 24.7311828 44.230769
## redIntWthChina=redIntWthChina_+ 30.9090909 32.692308
## nwsGdJbComCov=nwsGdJbComCov_+ 20.3539823 44.230769
## afrDie=afrDie_+ 13.6690647 73.076923
## covPlnnd=covPlnnd_= 20.0000000 42.307692
## flu=flu_+ 22.6666667 32.692308
## eldrNoBgDl=eldrNoBgDl_+ 44.4444444 15.384615
## rareNoWorr=rareNoWorr_+ 50.0000000 13.461538
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 16.3398693 48.076923
## thnksNoCovVacc=thnksNoCovVacc_= 22.7272727 28.846154
## eldrNoBgDl=eldrNoBgDl_= 19.0000000 36.538462
## stpCovStpImmi=stpCovStpImmi_+ 23.2142857 25.000000
## skeptInfoDocSci=skeptInfoDocSci_= 15.4411765 40.384615
## flu=flu_= 15.4471545 36.538462
## covVacEffRedVirus=covVacEffRedVirus_= 12.5874126 34.615385
## nwsGdJbComCov=nwsGdJbComCov_- 3.8043478 13.461538
## mdiaCovBgrDl=mdiaCovBgrDl_= 3.8095238 15.384615
## afrDie=afrDie_= 3.4090909 17.307692
## polBgDlIntrst=polBgDlIntrst_= 2.7522936 11.538462
## stpCovStpImmi=stpCovStpImmi_- 4.8076923 38.461538
## chnsCovRcst=chnsCovRcst_- 4.6620047 38.461538
## redIntWthChina=redIntWthChina_- 4.5146727 38.461538
## medOrgRecBstInt=medOrgRecBstInt_- 3.3175355 26.923077
## bgThrt=bgThrt_- 2.8061224 21.153846
## eldrNoBgDl=eldrNoBgDl_- 4.5955882 48.076923
## covNat=covNat_- 2.2346369 15.384615
## flu=flu_- 3.4482759 30.769231
## medOrgUntrust=medOrgUntrust_- 2.5287356 21.153846
## polBgDlIntrst=polBgDlIntrst_- 0.0000000 0.000000
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 3.3333333 32.692308
## rareNoWorr=rareNoWorr_- 3.9639640 42.307692
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 2.7368421 25.000000
## scntFkNwsCov=scntFkNwsCov_- 2.4070022 21.153846
## covVacEffRedVirus=covVacEffRedVirus_- 2.6639344 25.000000
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 2.6584867 25.000000
## mdiaCovBgrDl=mdiaCovBgrDl_- 0.8403361 5.769231
## covNgeenLab=covNgeenLab_- 1.8475751 15.384615
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 1.0443864 7.692308
## skeptInfoDocSci=skeptInfoDocSci_- 2.6000000 25.000000
## thnksNoCovVacc=thnksNoCovVacc_- 3.7931034 42.307692
## covPlnnd=covPlnnd_- 2.6565465 26.923077
## covNtSerPolSay=covNtSerPolSay_- 1.6842105 15.384615
## Global p.value
## covNtSerPolSay=covNtSerPolSay_+ 6.948640 5.995436e-30
## mdiaCovBgrDl=mdiaCovBgrDl_+ 14.350453 8.121159e-29
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ 8.912387 7.339612e-22
## scntFkNwsCov=scntFkNwsCov_+ 6.344411 1.333504e-21
## polBgDlIntrst=polBgDlIntrst_+ 28.549849 5.101205e-21
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ 5.135952 1.220572e-18
## covVacEffRedVirus=covVacEffRedVirus_+ 4.682779 2.203815e-18
## thnksNoCovVacc=thnksNoCovVacc_+ 2.416918 5.084634e-17
## covNgeenLab=covNgeenLab_+ 8.610272 5.375066e-17
## skeptInfoDocSci=skeptInfoDocSci_+ 3.927492 6.124722e-16
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 10.725076 3.302372e-15
## covPlnnd=covPlnnd_+ 3.776435 2.476126e-13
## covNat=covNat_+ 11.782477 6.461131e-13
## medOrgRecBstInt=medOrgRecBstInt_+ 6.042296 1.463690e-12
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ 3.021148 1.615108e-12
## medOrgUntrust=medOrgUntrust_+ 8.610272 2.742141e-12
## bgThrt=bgThrt_+ 12.084592 7.242746e-09
## chnsCovRcst=chnsCovRcst_+ 16.012085 1.442445e-08
## rareNoWorr=rareNoWorr_= 14.048338 2.877745e-08
## redIntWthChina=redIntWthChina_+ 8.308157 1.056336e-07
## nwsGdJbComCov=nwsGdJbComCov_+ 17.069486 1.712948e-06
## afrDie=afrDie_+ 41.993958 2.850537e-06
## covPlnnd=covPlnnd_= 16.616314 4.705136e-06
## flu=flu_+ 11.329305 1.592927e-05
## eldrNoBgDl=eldrNoBgDl_+ 2.719033 2.185660e-05
## rareNoWorr=rareNoWorr_+ 2.114804 2.954745e-05
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 23.111782 4.156170e-05
## thnksNoCovVacc=thnksNoCovVacc_= 9.969789 5.954591e-05
## eldrNoBgDl=eldrNoBgDl_= 15.105740 6.935312e-05
## stpCovStpImmi=stpCovStpImmi_+ 8.459215 1.724815e-04
## skeptInfoDocSci=skeptInfoDocSci_= 20.543807 6.784784e-04
## flu=flu_= 18.580060 1.461547e-03
## covVacEffRedVirus=covVacEffRedVirus_= 21.601208 2.450654e-02
## nwsGdJbComCov=nwsGdJbComCov_- 27.794562 1.240060e-02
## mdiaCovBgrDl=mdiaCovBgrDl_= 31.722054 6.192947e-03
## afrDie=afrDie_= 39.879154 3.329702e-04
## polBgDlIntrst=polBgDlIntrst_= 32.930514 2.810906e-04
## stpCovStpImmi=stpCovStpImmi_- 62.839879 2.327502e-04
## chnsCovRcst=chnsCovRcst_- 64.803625 6.399249e-05
## redIntWthChina=redIntWthChina_- 66.918429 1.376468e-05
## medOrgRecBstInt=medOrgRecBstInt_- 63.746224 2.357740e-08
## bgThrt=bgThrt_- 59.214502 7.667446e-09
## eldrNoBgDl=eldrNoBgDl_- 82.175227 5.116998e-09
## covNat=covNat_- 54.078550 2.631309e-09
## flu=flu_- 70.090634 1.307620e-09
## medOrgUntrust=medOrgUntrust_- 65.709970 9.593280e-12
## polBgDlIntrst=polBgDlIntrst_- 38.519637 2.685222e-12
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- 77.039275 1.194209e-12
## rareNoWorr=rareNoWorr_- 83.836858 7.036566e-13
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- 71.752266 3.758182e-13
## scntFkNwsCov=scntFkNwsCov_- 69.033233 1.571835e-13
## covVacEffRedVirus=covVacEffRedVirus_- 73.716012 2.230956e-14
## fllwRecMedOrgImp=fllwRecMedOrgImp_- 73.867069 1.775121e-14
## mdiaCovBgrDl=mdiaCovBgrDl_- 53.927492 1.600029e-14
## covNgeenLab=covNgeenLab_- 65.407855 1.343410e-14
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 57.854985 3.418612e-15
## skeptInfoDocSci=skeptInfoDocSci_- 75.528701 1.275918e-15
## thnksNoCovVacc=thnksNoCovVacc_- 87.613293 1.226534e-16
## covPlnnd=covPlnnd_- 79.607251 9.803130e-18
## covNtSerPolSay=covNtSerPolSay_- 71.752266 1.231666e-18
## v.test
## covNtSerPolSay=covNtSerPolSay_+ 11.368581
## mdiaCovBgrDl=mdiaCovBgrDl_+ 11.138794
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+ 9.608805
## scntFkNwsCov=scntFkNwsCov_+ 9.547121
## polBgDlIntrst=polBgDlIntrst_+ 9.407078
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+ 8.812801
## covVacEffRedVirus=covVacEffRedVirus_+ 8.746342
## thnksNoCovVacc=thnksNoCovVacc_+ 8.384721
## covNgeenLab=covNgeenLab_+ 8.378184
## skeptInfoDocSci=skeptInfoDocSci_+ 8.086811
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 7.878915
## covPlnnd=covPlnnd_+ 7.320194
## covNat=covNat_+ 7.190380
## medOrgRecBstInt=medOrgRecBstInt_+ 7.077887
## fllwRecMedOrgImp=fllwRecMedOrgImp_+ 7.064228
## medOrgUntrust=medOrgUntrust_+ 6.990343
## bgThrt=bgThrt_+ 5.785197
## chnsCovRcst=chnsCovRcst_+ 5.668272
## rareNoWorr=rareNoWorr_= 5.548716
## redIntWthChina=redIntWthChina_+ 5.316756
## nwsGdJbComCov=nwsGdJbComCov_+ 4.784639
## afrDie=afrDie_+ 4.681306
## covPlnnd=covPlnnd_= 4.577523
## flu=flu_+ 4.315430
## eldrNoBgDl=eldrNoBgDl_+ 4.245028
## rareNoWorr=rareNoWorr_+ 4.176926
## fllwRecMedOrgImp=fllwRecMedOrgImp_= 4.098622
## thnksNoCovVacc=thnksNoCovVacc_= 4.014603
## eldrNoBgDl=eldrNoBgDl_= 3.978494
## stpCovStpImmi=stpCovStpImmi_+ 3.756245
## skeptInfoDocSci=skeptInfoDocSci_= 3.398131
## flu=flu_= 3.182212
## covVacEffRedVirus=covVacEffRedVirus_= 2.249094
## nwsGdJbComCov=nwsGdJbComCov_- -2.500535
## mdiaCovBgrDl=mdiaCovBgrDl_= -2.737387
## afrDie=afrDie_= -3.588199
## polBgDlIntrst=polBgDlIntrst_= -3.632131
## stpCovStpImmi=stpCovStpImmi_- -3.680529
## chnsCovRcst=chnsCovRcst_- -3.997583
## redIntWthChina=redIntWthChina_- -4.347582
## medOrgRecBstInt=medOrgRecBstInt_- -5.583464
## bgThrt=bgThrt_- -5.775611
## eldrNoBgDl=eldrNoBgDl_- -5.843322
## covNat=covNat_- -5.953087
## flu=flu_- -6.066458
## medOrgUntrust=medOrgUntrust_- -6.812476
## polBgDlIntrst=polBgDlIntrst_- -6.993286
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_- -7.106039
## rareNoWorr=rareNoWorr_- -7.178723
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_- -7.263996
## scntFkNwsCov=scntFkNwsCov_- -7.380934
## covVacEffRedVirus=covVacEffRedVirus_- -7.636564
## fllwRecMedOrgImp=fllwRecMedOrgImp_- -7.665950
## mdiaCovBgrDl=mdiaCovBgrDl_- -7.679266
## covNgeenLab=covNgeenLab_- -7.701630
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- -7.874590
## skeptInfoDocSci=skeptInfoDocSci_- -7.996899
## thnksNoCovVacc=thnksNoCovVacc_- -8.280506
## covPlnnd=covPlnnd_- -8.576232
## covNtSerPolSay=covNtSerPolSay_- -8.811787save(res_mfa_quali, res.hcpc, file='./data/mfa_and_classification_onqualidata.rdata')Analysis
Boxplot of choice covmis per group
df_covmis$covqual_class <- res.hcpc$data.clust$clust
freq(df_covmis$covqual_class)## n % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3 52 7.9 7.9df_covmis %>%
transmute_at(.vars = vars(starts_with("covmis")),
~fct_recode(.x %>% as_factor,
"-"="1",
"-"="2",
"="="3",
"="="4",
"+"="5",
"+"="6")) %>%
cbind(df,.) %>%
mutate(covqual_class=res.hcpc$data.clust$clust) %>%
gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
mutate(Covmis_var=str_remove(Covmis_var,"^covmis_"),
Covmis_res=factor(Covmis_res,
levels = c("+","=","-"))) %>%
ggplot(., aes(x = covqual_class, fill=Covmis_res)) +
scale_fill_manual(values=c("green", "blue", "red")) +
geom_bar(position='fill') +
facet_wrap(~ Covmis_var, ncol = 5)
mean of choice covmis per group
df_mean_per_cat <- df_covmis %>%
dplyr::select(matches("^covmis_")) %>%
mutate(covmis_cat=df_covmis$covqual_class) %>%
group_by(covmis_cat) %>%
summarise_all(mean)
df_mean_per_cat## # A tibble: 3 × 27
## covmis_cat covmis_att_flu covmis_att_afrDie covmis_att_eldrN… covmis_att_rare…
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 1.86 3.69 1.37 1.26
## 2 2 2.73 4.33 2.31 2.23
## 3 3 3.67 5.04 2.79 2.87
## # … with 22 more variables: covmis_att_bgThrt <dbl>,
## # covmis_cnsp_ctiusAsian <dbl>, covmis_cnsp_stpCovStpImmi <dbl>,
## # covmis_cnsp_redIntWthChina <dbl>, covmis_cnsp_chnsCovRcst <dbl>,
## # covmis_orgn_covPlnnd <dbl>, covmis_orgn_covNat <dbl>,
## # covmis_orgn_covNgeenLab <dbl>, covmis_orgn_scntFkNwsCov <dbl>,
## # covmis_pltc_polBgDlIntrst <dbl>, covmis_pltc_covNtSerPolSay <dbl>,
## # covmis_pltc_polDwnplCovPlpLDngr <dbl>, covmis_cvrg_mdiaCovBgrDl <dbl>, …questionr::freq(df_covmis$covmis_cat)## [1] n % val%
## <0 rows> (or 0-length row.names)df_covmis$HH_INCOME_TEXT <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $10,000"="1",
"$10,000 to $30,000"="2",
"$30,000 to $50,000"="3",
"$50,000 to $70,000"="4",
"$70,000 to $100,000"="5",
"$100,000 to $200,000"="6",
"$200,000 to $500,000"="7",
"$500,000 or more"="8")
table(df_covmis$HH_INCOME_TEXT, df_covmis$covqual_class) %>% cprop %>% xtable::xtable()## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun 7 15:12:24 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
## \hline
## & 1 & 2 & 3 & All \\
## \hline
## Less than \$10,000 & 8.24 & 11.18 & 17.31 & 9.67 \\
## \$10,000 to \$30,000 & 22.27 & 31.06 & 23.08 & 24.47 \\
## \$30,000 to \$50,000 & 18.71 & 16.15 & 21.15 & 18.28 \\
## \$50,000 to \$70,000 & 15.37 & 16.15 & 13.46 & 15.41 \\
## \$70,000 to \$100,000 & 15.59 & 11.18 & 11.54 & 14.20 \\
## \$100,000 to \$200,000 & 14.25 & 9.32 & 11.54 & 12.84 \\
## \$200,000 to \$500,000 & 4.90 & 3.73 & 1.92 & 4.38 \\
## \$500,000 or more & 0.67 & 1.24 & 0.00 & 0.76 \\
## Total & 100.00 & 100.00 & 100.00 & 100.00 \\
## \hline
## \end{tabular}
## \end{table}Demo cluster
table(df_covmis$demo_class, df_covmis$covqual_class) %>% cprop##
## 1 2 3 All
## 1 33.6 26.7 40.4 32.5
## 2 22.9 20.5 17.3 21.9
## 3 10.9 5.6 3.8 9.1
## 4 5.3 7.5 5.8 5.9
## 5 3.3 1.9 1.9 2.9
## 6 16.5 20.5 17.3 17.5
## 7 7.3 17.4 13.5 10.3
## Total 100.0 100.0 100.0 100.0table(df_covmis$demo_class, df_covmis$covqual_class) %>% t.test()##
## One Sample t-test
##
## data: .
## t = 3.8595, df = 20, p-value = 0.0009769
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 14.48614 48.56148
## sample estimates:
## mean of x
## 31.52381Logistiaue regression
library(ordinal)##
## Attaching package: 'ordinal'## The following object is masked from 'package:dplyr':
##
## slicelibrary(nnet)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(gtsummary)
library(ggeffects)
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterWe’ll do it on the demo classification and then on the other demographic data
df_covmis$covqual_class <- factor(df_covmis$covqual_class, c("1","2","3"))
freq(df_covmis$covqual_class)## n % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3 52 7.9 7.9df_covmis$demo_class <- df_covmis$demo_class %>% as.factor()
regm <- clm(covqual_class ~ demo_class, data = df_covmis)
summary(regm)## formula: covqual_class ~ demo_class
## data: df_covmis
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 662 -524.90 1065.80 5(0) 1.20e-10 6.9e+01
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## demo_class2 -0.08559 0.23406 -0.366 0.71460
## demo_class3 -0.68189 0.36403 -1.873 0.06105 .
## demo_class4 0.31483 0.35300 0.892 0.37246
## demo_class5 -0.49409 0.58000 -0.852 0.39428
## demo_class6 0.23243 0.24033 0.967 0.33348
## demo_class7 0.78221 0.27097 2.887 0.00389 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 1|2 0.8172 0.1488 5.491
## 2|3 2.5662 0.1920 13.363tbl_regression(regm, exponentiate = TRUE)| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| demo_class | |||
| 1 | — | — | |
| 2 | 0.92 | 0.58, 1.45 | 0.7 |
| 3 | 0.51 | 0.24, 1.00 | 0.061 |
| 4 | 1.37 | 0.67, 2.70 | 0.4 |
| 5 | 0.61 | 0.17, 1.75 | 0.4 |
| 6 | 1.26 | 0.79, 2.02 | 0.3 |
| 7 | 2.19 | 1.28, 3.72 | 0.004 |
| 1 OR = Odds Ratio, CI = Confidence Interval | |||
ggcoef_model(
regm,
exponentiate = TRUE
)
plot(ggeffect(regm, "demo_class"))## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSE
df_covmis$covqual_class <- df_covmis$covqual_class %>%
as.character() %>%
as.numeric()
ggpubr::ggboxplot(df_covmis, x="demo_class", y="covqual_class")
res.aov <- anova_test(covqual_class ~ demo_class, data = df_covmis)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 demo_class 6 655 2.665 0.015 * 0.024pwc <- df_covmis %>% tukey_hsd(covqual_class ~ demo_class)
pwc## # A tibble: 21 × 9
## term group1 group2 null.value estimate conf.low conf.high p.adj p.adj.signif
## * <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 demo… 1 2 0 -0.0436 -0.243 0.155 0.995 ns
## 2 demo… 1 3 0 -0.179 -0.449 0.0916 0.445 ns
## 3 demo… 1 4 0 0.0662 -0.256 0.388 0.997 ns
## 4 demo… 1 5 0 -0.132 -0.575 0.311 0.975 ns
## 5 demo… 1 6 0 0.0443 -0.169 0.258 0.996 ns
## 6 demo… 1 7 0 0.222 -0.0353 0.480 0.143 ns
## 7 demo… 2 3 0 -0.135 -0.419 0.149 0.799 ns
## 8 demo… 2 4 0 0.110 -0.224 0.444 0.96 ns
## 9 demo… 2 5 0 -0.0886 -0.540 0.363 0.997 ns
## 10 demo… 2 6 0 0.0879 -0.143 0.319 0.92 ns
## # … with 11 more rowspwc <- pwc %>% add_xy_position(x = "demo_class")
ggpubr::ggboxplot(df_covmis, x = "demo_class", y = "covqual_class") +
ggpubr::stat_pvalue_manual(pwc, hide.ns = TRUE) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc)
)
df_covmis$covqual_class <- factor(df_covmis$covqual_class, c("1","2","3"))
freq(df_covmis$covqual_class)## n % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3 52 7.9 7.9df_covmis$CONTINENT_BORN_TEXT_1 <- relevel(df_covmis$CONTINENT_BORN_TEXT_1 %>% as.factor(), "USA")
df_covmis$DOB_YEAR_PERIODE <- relevel(df_covmis$DOB_YEAR_PERIODE %>% as.factor(), "(1995,2005]")
df_covmis$EDUCATION_2_TEXT <- fct_recode(df$EDUCATION_1 %>% as.character,
"No college degree"="1",
"No college degree"="2",
"No college degree"="3",
"College degree"="4",
"College degree"="5",
"Graduate degree"="6",
"Graduate degree"="7",
"Graduate degree"="8")
df_covmis$EDUCATION_2_TEXT %>% freq()## n % val%
## No college degree 276 41.7 41.7
## College degree 257 38.8 38.8
## Graduate degree 129 19.5 19.5regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT, data = df_covmis)## # weights: 72 (46 variable)
## initial value 720.689661
## iter 10 value 490.757326
## iter 20 value 484.003154
## iter 30 value 483.035043
## iter 40 value 482.776302
## iter 50 value 482.772030
## final value 482.772018
## convergedsummary(regm)## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 +
## HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT,
## data = df_covmis)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 2 -0.7399172 1.2026244 0.5992759
## 3 -3.5324654 0.7623684 1.8405444
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 2 0.07737533 46.93527
## 3 1.60638178 48.61955
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2 -0.1527146
## 3 0.1859194
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 2 -0.4923584 -38.954510
## 3 0.6260380 1.942831
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 2 -0.679238 -35.367263
## 3 -24.807752 -8.539058
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 2 -47.51973 -0.9291578
## 3 -24.75700 -0.6124034
## HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 2 -1.0091765 0.1253643 0.3094846
## 3 0.1452345 -36.7817417 0.1294950
## DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 2 0.2322808 -0.06440325
## 3 -0.8686300 0.67046172
## DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 2 0.06796774 0.6987227 -28.83694 -0.2369775
## 3 -0.19805022 0.2537439 -20.26495 -38.0138254
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2 -0.4025703 -0.5461438
## 3 -0.2790196 -0.9337707
##
## Std. Errors:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 2 0.5923249 0.6616935 0.2946185
## 3 0.9877753 1.2069043 0.5935465
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 2 0.6382346 0.8548434
## 3 0.9863467 0.8548434
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2 0.6032795
## 3 0.8835034
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 2 0.5659516 5.001539e-15
## 3 0.9749935 1.654742e+00
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 2 1.290244e+00 6.760417e-16
## 3 1.011613e-11 1.411400e-05
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 2 9.792997e-16 0.5907222
## 3 9.046577e-12 0.8994333
## HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 2 0.5293139 8.570257e-01 0.5030957
## 3 0.7990807 2.612310e-17 0.7083435
## DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 2 0.493491 0.3700348
## 3 1.074976 0.4547040
## DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 2 0.2596986 0.2015267 3.469577e-15 1.138045e+00
## 3 0.4169129 0.3154862 2.380610e-11 9.897664e-18
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2 0.2335385 0.3177511
## 3 0.3556798 0.5255403
##
## Residual Deviance: 965.544
## AIC: 1057.544tbl_regression(regm, exponentiate = TRUE)## ℹ Multinomial models have a different underlying structure than the models
## gtsummary was designed for. Other gtsummary functions designed to work with
## tbl_regression objects may yield unexpected results.| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| 2 | |||
| EXPGRP_TEXT | |||
| Chinese | — | — | |
| Non-Chinese Asian | 3.33 | 0.91, 12.2 | 0.069 |
| White | 1.82 | 1.02, 3.24 | 0.042 |
| CONTINENT_BORN_TEXT_1 | |||
| USA | — | — | |
| 4Tigers and Japan | 1.08 | 0.31, 3.77 | >0.9 |
| Africa | 241,951,808,177,338,089,472 | 45,298,605,201,129,431,040, 1,292,328,477,231,431,548,928 | <0.001 |
| Central Eastern Europe | 0.86 | 0.26, 2.80 | 0.8 |
| Developping Asia | 0.61 | 0.20, 1.85 | 0.4 |
| Middle East | 0.00 | 0.00, 0.00 | <0.001 |
| North America | 0.51 | 0.04, 6.36 | 0.6 |
| Oceania | 0.00 | 0.00, 0.00 | <0.001 |
| South America | 0.00 | 0.00, 0.00 | <0.001 |
| Western Europe | 0.39 | 0.12, 1.26 | 0.12 |
| HAS_LIVED_USA | |||
| FALSE | — | — | |
| TRUE | 0.36 | 0.13, 1.03 | 0.057 |
| DOB_YEAR_PERIODE | |||
| (1995,2005] | — | — | |
| (1945,1955] | 1.13 | 0.21, 6.08 | 0.9 |
| (1955,1965] | 1.36 | 0.51, 3.65 | 0.5 |
| (1965,1975] | 1.26 | 0.48, 3.32 | 0.6 |
| (1975,1985] | 0.94 | 0.45, 1.94 | 0.9 |
| (1985,1995] | 1.07 | 0.64, 1.78 | 0.8 |
| SEX_TEXT | |||
| Female | — | — | |
| Male | 2.01 | 1.35, 2.99 | <0.001 |
| Other | 0.00 | 0.00, 0.00 | <0.001 |
| Transgender | 0.79 | 0.08, 7.34 | 0.8 |
| EDUCATION_2_TEXT | |||
| No college degree | — | — | |
| College degree | 0.67 | 0.42, 1.06 | 0.085 |
| Graduate degree | 0.58 | 0.31, 1.08 | 0.086 |
| 3 | |||
| EXPGRP_TEXT | |||
| Chinese | — | — | |
| Non-Chinese Asian | 2.14 | 0.20, 22.8 | 0.5 |
| White | 6.30 | 1.97, 20.2 | 0.002 |
| CONTINENT_BORN_TEXT_1 | |||
| USA | — | — | |
| 4Tigers and Japan | 4.98 | 0.72, 34.5 | 0.10 |
| Africa | 1,303,779,557,693,494,919,168 | 244,095,697,809,265,033,216, 6,963,830,785,693,286,662,144 | <0.001 |
| Central Eastern Europe | 1.20 | 0.21, 6.80 | 0.8 |
| Developping Asia | 1.87 | 0.28, 12.6 | 0.5 |
| Middle East | 6.98 | 0.27, 179 | 0.2 |
| North America | 0.00 | 0.00, 0.00 | <0.001 |
| Oceania | 0.00 | 0.00, 0.00 | <0.001 |
| South America | 0.00 | 0.00, 0.00 | <0.001 |
| Western Europe | 0.54 | 0.09, 3.16 | 0.5 |
| HAS_LIVED_USA | |||
| FALSE | — | — | |
| TRUE | 1.16 | 0.24, 5.54 | 0.9 |
| DOB_YEAR_PERIODE | |||
| (1995,2005] | — | — | |
| (1945,1955] | 0.00 | 0.00, 0.00 | <0.001 |
| (1955,1965] | 1.14 | 0.28, 4.56 | 0.9 |
| (1965,1975] | 0.42 | 0.05, 3.45 | 0.4 |
| (1975,1985] | 1.96 | 0.80, 4.77 | 0.14 |
| (1985,1995] | 0.82 | 0.36, 1.86 | 0.6 |
| SEX_TEXT | |||
| Female | — | — | |
| Male | 1.29 | 0.69, 2.39 | 0.4 |
| Other | 0.00 | 0.00, 0.00 | <0.001 |
| Transgender | 0.00 | 0.00, 0.00 | <0.001 |
| EDUCATION_2_TEXT | |||
| No college degree | — | — | |
| College degree | 0.76 | 0.38, 1.52 | 0.4 |
| Graduate degree | 0.39 | 0.14, 1.10 | 0.076 |
| 1 OR = Odds Ratio, CI = Confidence Interval | |||
ggcoef_multinom(
regm,
exponentiate = TRUE
)
f_normfactor <- function(v){
res <- AMBI::f_normalisation(v)
res <- res*3
res <- cut(res, c(0,1,2,3), labels=c('weak','middle','high'))
return(res)
}df_covmis$neuroticism_qual <- f_normfactor(df_covmis$AMBI_BIG5_Neuroticism)
df_covmis$extraversion_qual <- f_normfactor(df_covmis$AMBI_BIG5_Extraversion)
df_covmis$openness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Openness)
df_covmis$conscientiousness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Conscientiousness)
df_covmis$agreeableness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Agreeableness)
df_covmis$covqual_class <- relevel(df_covmis$covqual_class %>% as.factor(), "2")
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)## # weights: 102 (66 variable)
## initial value 715.196600
## iter 10 value 457.299787
## iter 20 value 446.412279
## iter 30 value 444.615073
## iter 40 value 444.201677
## iter 50 value 444.039935
## iter 60 value 444.011632
## final value 444.011492
## convergedsummary(regm)## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 +
## HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT +
## neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual, data = df_covmis)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1 1.738012 -1.1158803 -0.9598885
## 3 -3.663480 -0.2070407 1.5813282
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1 -0.3275979 -19.12281
## 3 1.8344488 -12.54272
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1 0.0466284
## 3 0.3779654
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1 0.4631373 22.61849
## 3 1.5124886 24.71378
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1 0.9716688 16.7287749
## 3 -10.3027941 -0.5179976
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1 15.228505 0.8530807
## 3 -3.830835 0.1679522
## HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 1 0.788691 -0.1840107 -0.3438741
## 3 1.004703 -19.4402105 -0.2933579
## DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 1 -0.06760224 0.2610123
## 3 -0.89486498 0.9009455
## DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1 0.05080513 -0.5156186 15.579950 0.1985237
## 3 -0.01288729 -0.4149011 -1.940545 -17.6515469
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 1 0.29195800 0.3656797
## 3 0.01285511 -0.6797506
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1 0.8226438 0.7609507 -0.4316414
## 3 0.9514179 0.5052454 0.1870162
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1 0.07198186 -0.3984916 0.5463803
## 3 0.65784636 -0.8193364 -0.9644435
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 1 -0.09763558 -0.02900892
## 3 -0.00575730 0.57150202
## agreeableness_qualmiddle agreeableness_qualhigh
## 1 -1.2024486 -0.01251068
## 3 0.2723946 0.81165224
##
## Std. Errors:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1 1.083845 0.6888784 0.3167776
## 3 2.003122 1.2736420 0.7282255
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1 0.660478 5.400441e-09
## 3 1.144425 2.186272e-06
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1 0.6349121
## 3 0.9953854
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1 0.5888515 0.9152782
## 3 1.1460802 0.9152782
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1 1.301694e+00 3.301291e-08
## 3 1.808779e-05 9.562324e-10
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1 9.158028e-08 0.6166259
## 3 1.155500e-09 0.9949234
## HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 1 0.5493611 9.241718e-01 0.5535269
## 3 0.8797301 2.946859e-09 0.8339505
## DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 1 0.5288291 0.4009660
## 3 1.1443785 0.5527925
## DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1 0.2768421 0.2249110 1.118505e-08 1.165827e+00
## 3 0.4700206 0.3786254 5.610860e-10 1.551336e-08
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 1 0.2494315 0.3413472
## 3 0.4086299 0.5993647
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1 0.3446849 0.4147411 0.2984756
## 3 0.5588578 0.7046277 0.5148896
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1 0.4434444 0.4846603 0.5154640
## 3 0.7293981 0.7069991 0.7737271
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 1 0.3360337 0.4154541
## 3 0.6036637 0.6914744
## agreeableness_qualmiddle agreeableness_qualhigh
## 1 0.618170 0.6949397
## 3 1.315696 1.4037369
##
## Residual Deviance: 888.023
## AIC: 1020.023table(df_covmis$EDUCATION_2_TEXT, df_covmis$EXPGRP_TEXT) %>% lprop() %>% xtable::xtable()## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun 7 15:13:01 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
## \hline
## & Chinese & Non-Chinese Asian & White & Total \\
## \hline
## No college degree & 27.17 & 0.72 & 72.10 & 100.00 \\
## College degree & 36.19 & 2.72 & 61.09 & 100.00 \\
## Graduate degree & 44.19 & 4.65 & 51.16 & 100.00 \\
## All & 33.99 & 2.27 & 63.75 & 100.00 \\
## \hline
## \end{tabular}
## \end{table}ggcoef_multinom(
regm,
exponentiate = TRUE
)
the variable on the age is long, we shall try to shorten it and cross it with education variable (young people without a college degree are socially different than older on without a college degree)
df_covmis$DOB_YEAR_PERIODE <- df_covmis$DOB_YEAR %>% cut(breaks = c(1944,1955,1965,1975,1985,1995,2005))
df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
"-25y"="(1995,2005]",
"25 - 45y"="(1985,1995]",
"25 - 45y"="(1975,1985]",
"+45y"="(1944,1955]",
"+45y"="(1955,1965]",
"+45y"="(1965,1975]")
df_covmis <- df_covmis %>% unite("age_education", c("DOB_AGE_BRACKET","EDUCATION_2_TEXT"))
df_covmis$age_education <- factor(df_covmis$age_education, levels=c('-25y_No college degree',
"25 - 45y_No college degree",
'+45y_No college degree',
"-25y_College degree",
"25 - 45y_College degree",
"+45y_College degree",
"-25y_Graduate degree",
"25 - 45y_Graduate degree",
"+45y_Graduate degree"))
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)## # weights: 105 (68 variable)
## initial value 716.295212
## iter 10 value 453.008834
## iter 20 value 443.820709
## iter 30 value 441.905424
## iter 40 value 441.421874
## iter 50 value 441.268173
## iter 60 value 441.254707
## final value 441.254611
## convergedsummary(regm)## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 +
## HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual +
## extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual, data = df_covmis)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1 1.858675 -1.0437121 -0.9098322
## 3 -3.721566 -0.2958922 1.6784329
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1 -0.3486255 -17.73722
## 3 1.9114099 -13.35145
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1 -0.02777399
## 3 0.44043085
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1 0.4876404 21.82408
## 3 1.3881898 23.64850
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1 1.112134 15.3896377
## 3 -10.564591 -0.5458462
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1 14.352886 0.91341919
## 3 -4.005214 0.05695805
## HAS_LIVED_USATRUE SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1 0.8112177 -0.5616381 14.665945 0.1890714
## 3 0.9705948 -0.4567149 -1.191577 -16.6749331
## age_education25 - 45y_No college degree age_education+45y_No college degree
## 1 -0.314218 -0.9636291
## 3 0.450312 -0.4569733
## age_education-25y_College degree age_education25 - 45y_College degree
## 1 -0.1792953 0.2960378
## 3 0.2420838 0.3449017
## age_education+45y_College degree age_education-25y_Graduate degree
## 1 1.15068438 0.03518851
## 3 0.04766783 -0.28454612
## age_education25 - 45y_Graduate degree age_education+45y_Graduate degree
## 1 0.3763641 -0.1172915
## 3 -0.1192872 -19.8537947
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1 0.8774775 0.8325559 -0.4442107
## 3 0.8788918 0.3739392 0.2101593
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1 0.01771684 -0.4495745 0.4855999
## 3 0.67781227 -0.8799866 -1.0053462
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 1 -0.068907063 -0.02756163
## 3 0.007027449 0.63413752
## agreeableness_qualmiddle agreeableness_qualhigh
## 1 -1.188226 0.008055108
## 3 0.303773 0.733355256
##
## Std. Errors:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1 1.086954 0.6804273 0.3201156
## 3 2.018067 1.2856348 0.7431532
## CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1 0.6639521 1.792701e-08
## 3 1.1438946 1.369105e-06
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1 0.6366510
## 3 0.9981343
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1 0.5906057 0.9265872
## 3 1.1620097 0.9265872
## CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1 1.303460e+00 1.070377e-07
## 3 1.636786e-05 3.748516e-09
## CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1 1.995566e-07 0.6160382
## 3 2.694570e-09 0.9852024
## HAS_LIVED_USATRUE SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1 0.5476758 0.2268507 2.627758e-08 1.185345e+00
## 3 0.8756592 0.3788061 1.731246e-09 3.664013e-08
## age_education25 - 45y_No college degree age_education+45y_No college degree
## 1 0.4213367 0.5315782
## 3 0.6094198 0.8356615
## age_education-25y_College degree age_education25 - 45y_College degree
## 1 0.3268457 0.3285704
## 3 0.5814401 0.5505411
## age_education+45y_College degree age_education-25y_Graduate degree
## 1 0.8270658 0.6505728
## 3 1.3422018 1.2136692
## age_education25 - 45y_Graduate degree age_education+45y_Graduate degree
## 1 0.3831647 7.260942e-01
## 3 0.6744568 4.221120e-09
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1 0.3455548 0.4144532 0.3015682
## 3 0.5556366 0.6985694 0.5154151
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1 0.4472320 0.4905583 0.5220824
## 3 0.7220587 0.7064663 0.7716673
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 1 0.3375349 0.4168743
## 3 0.6064400 0.6879759
## agreeableness_qualmiddle agreeableness_qualhigh
## 1 0.6170652 0.6931013
## 3 1.2994331 1.3879572
##
## Residual Deviance: 882.5092
## AIC: 1018.509ggcoef_multinom(
regm,
exponentiate = TRUE
)
Now we shall consider only two groups, those in the first and second group, and those in the third group.
df_covmis$covqual_class_2 <- fct_recode(df_covmis$covqual_class %>% as.character,
"Believers"="1",
"Believers"="2",
"Skeptics"="3")
freq(df_covmis$covqual_class_2)## n % val%
## Believers 610 92.1 92.1
## Skeptics 52 7.9 7.9reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
age_education + neuroticism_qual + extraversion_qual + openness_qual +
conscientiousness_qual + agreeableness_qual, data = df_covmis, family = binomial(logit))
reg##
## Call: glm(formula = covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 +
## HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual +
## extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual, family = binomial(logit), data = df_covmis)
##
## Coefficients:
## (Intercept)
## -5.75877
## EXPGRP_TEXTNon-Chinese Asian
## 0.34362
## EXPGRP_TEXTWhite
## 2.34901
## CONTINENT_BORN_TEXT_14Tigers and Japan
## 2.17530
## CONTINENT_BORN_TEXT_1Africa
## -15.75584
## CONTINENT_BORN_TEXT_1Central Eastern Europe
## 0.33159
## CONTINENT_BORN_TEXT_1Developping Asia
## 1.03774
## CONTINENT_BORN_TEXT_1Middle East
## 2.54159
## CONTINENT_BORN_TEXT_1North America
## -14.66870
## CONTINENT_BORN_TEXT_1Oceania
## -12.38292
## CONTINENT_BORN_TEXT_1South America
## -14.99015
## CONTINENT_BORN_TEXT_1Western Europe
## -0.56002
## HAS_LIVED_USATRUE
## 0.43110
## SEX_TEXTMale
## -0.08644
## SEX_TEXTOther
## -13.61811
## SEX_TEXTTransgender
## -15.28029
## age_education25 - 45y_No college degree
## 0.64883
## age_education+45y_No college degree
## 0.17247
## age_education-25y_College degree
## 0.40673
## age_education25 - 45y_College degree
## 0.13832
## age_education+45y_College degree
## -0.86501
## age_education-25y_Graduate degree
## -0.32793
## age_education25 - 45y_Graduate degree
## -0.39631
## age_education+45y_Graduate degree
## -15.51461
## neuroticism_qualmiddle
## 0.27063
## neuroticism_qualhigh
## -0.20500
## extraversion_qualmiddle
## 0.51339
## extraversion_qualhigh
## 0.64802
## openness_qualmiddle
## -0.59504
## openness_qualhigh
## -1.38074
## conscientiousness_qualmiddle
## 0.01918
## conscientiousness_qualhigh
## 0.61368
## agreeableness_qualmiddle
## 1.21817
## agreeableness_qualhigh
## 0.76502
##
## Degrees of Freedom: 651 Total (i.e. Null); 618 Residual
## (10 observations deleted due to missingness)
## Null Deviance: 352.9
## Residual Deviance: 307.2 AIC: 375.2ggcoef_model(reg, exponentiate = TRUE)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
which(is.na(df_covmis$extraversion_qual))## [1] 585df_covmis[585,]## X.4 X.3 X.1 X Unnamed..0 StartDate EndDate Status
## 585 585 585 585 584 586 6/11/2020 17:38 6/11/2020 18:27 0
## IPAddress Progress Duration..in.seconds. Finished RecordedDate
## 585 69.62.136.135 100 2940 1 6/11/2020 18:27
## ResponseId RecipientLastName RecipientFirstName RecipientEmail
## 585 R_1Fktpj7qrFl5Z5M NA NA NA
## ExternalReference LocationLatitude LocationLongitude DistributionChannel
## 585 NA 38.4207 -121.3623 anonymous
## UserLanguage Q_RecaptchaScore PROLIFICID CONSENT EXPGRP
## 585 EN 0.9 5de53996227eed0b8dd9c44d 1 1
## CHIN_SPECIFIC CHIN_SPECIFIC_6_TEXT NONASIA_SPECIFIC
## 585 NA NA
## NONASIA_SPECIFIC_19_TEXT NonWNonA_Specific NonWNonA_Specific_7_TEXT
## 585 NA NA NA
## NonWNonA_Specific_14_TEXT OTHER_RACE_1 OTHER_RACE_2 OTHER_RACE_3
## 585 NA -99 -99 -99
## OTHER_RACE_4 OTHER_RACE_5 OTHER_RACE_6 OTHER_RACE_7 OTHER_RACE_4_TEXT
## 585 -99 -99 -99 -99 -99
## OTHER_RACE_5_TEXT OTHER_RACE_6_TEXT OTHER_RACE_7_TEXT DOB_1 DOB_2 DOB_3 SEX
## 585 -99 -99 -99 9 14 89 2
## SEX_4_TEXT EDUCATION_1 EDUCATION_2 EDUCATION_3 COUNTRY_BORN
## 585 -99 4 4 3 1
## COUNTRY_BORN_2_TEXT BIRTHCTRY_RESIDENCE OTHER_COUNTRIES.1_1_TEXT
## 585 -99 31 -99
## OTHER_COUNTRIES.1_1_1 OTHER_COUNTRIES.1_2_TEXT OTHER_COUNTRIES.1_2_1
## 585 -99 -99 -99
## OTHER_COUNTRIES.1_3_TEXT OTHER_COUNTRIES.1_3_1 OTHER_COUNTRIES.1_4_TEXT
## 585 -99 -99 -99
## OTHER_COUNTRIES.1_4_1 OTHER_COUNTRIES.1_5_TEXT OTHER_COUNTRIES.1_5_1
## 585 -99 -99 -99
## OTHER_COUNTRIES.1_6_TEXT OTHER_COUNTRIES.1_6_1 OTHER_COUNTRIES.1_7_TEXT
## 585 -99.0 -99 -99.0
## OTHER_COUNTRIES.1_7_1 OTHER_COUNTRIES.1_8_TEXT OTHER_COUNTRIES.1_8_1
## 585 -99 -99.0 -99
## FIRST_LANGUAGE FIRST_LANGUAGE_2_TEXT OTHER_LANGUAGES OCCUPATION HH_INCOME
## 585 1 -99 -99 Cook 2
## COVSCRN01 COVSCRN02_P COVSCRN03 COVSCRN04 COVSCRN04_1_TEXT COVSCRN05
## 585 3 NA NA NA NA NA
## COVSCRN05_1_TEXT COVSCRN02_N BAI_TIME_First.Click BAI_TIME_Last.Click
## 585 NA NA 6.22 40.369
## BAI_TIME_Page.Submit BAI_TIME_Click.Count BAI_1 BAI_2 BAI_3 BAI_4 BAI_5
## 585 41.317 26 3 4 4 4 4
## BAI_6 BAI_7 BAI_8 BAI_9 BAI_10 BAI_11 BAI_12 BAI_13 BAI_14 BAI_15 BAI_16
## 585 2 4 4 4 4 2 2 3 4 4 4
## BAI_17 BAI_18 BAI_19 BAI_20 BAI_21 BDI_TIME_First.Click BDI_TIME_Last.Click
## 585 4 4 2 3 4 3.877 161.515
## BDI_TIME_Page.Submit BDI_TIME_Click.Count BDI01 BDI02 BDI03 BDI04 BDI05
## 585 162.615 47 4 4 4 4 4
## BDI06 BDI07 BDI08 BDI09 BDI10 BDI11 BDI12 BDI13 BDI14 BDI15 BDI16 BDI17
## 585 4 4 4 4 3 3 4 4 4 3 4 4
## BDI18 BDI19 BDI19a BDI20 BDI21 COVATT_1 COVATT_2 COVATT_3 COVATT_4 COVATT_5
## 585 3 2 1 4 4 1 6 1 1 6
## COVCONSP_1 COVCONSP_2 COVCONSP_3 COVCONSP_4 COVORIGIN_1 COVORIGIN_2
## 585 1 1 1 6 1 6
## COVORIGIN_3 COVORIGIN_4 COVPOLITICS_1 COVPOLITICS_2 COVPOLITICS_3
## 585 1 1 1 1 6
## COVCOVERAGE_1 COVCOVERAGE_2 COVCOVERAGE_3 COVANTIVACC_1 COVANTIVACC_2
## 585 1 5 1 1 1
## COVANTIVACC_3 COVMEDSKEP_1 COVMEDSKEP_2 COVMEDSKEP_3 COVMEDSKEP_4
## 585 6 1 1 6 6
## AMBI_W1_TIME_First.Click AMBI_W1_TIME_Last.Click AMBI_W1_TIME_Page.Submit
## 585 3.47 171.162 172.25
## AMBI_W1_TIME_Click.Count AMBI_W1_1 AMBI_W1_2 AMBI_W1_3 AMBI_W1_4 AMBI_W1_5
## 585 60 1 1 1 1 1
## AMBI_W1_6 AMBI_W1_7 AMBI_W1_8 AMBI_W1_9 AMBI_W1_10 AMBI_W1_11 AMBI_W1_12
## 585 1 1 4 3 4 1 5
## AMBI_W1_13 AMBI_W1_14 AMBI_W1_15 AMBI_W1_16 AMBI_W1_17 AMBI_W1_18
## 585 5 5 1 1 1 1
## AMBI_W1_19 AMBI_W1_20 AMBI_W1_21 AMBI_W1_22 AMBI_W1_23 AMBI_W1_24
## 585 5 1 5 1 4 5
## AMBI_W1_25 AMBI_W1_26 AMBI_W1_27 AMBI_W1_28 AMBI_W1_29 AMBI_W1_30
## 585 5 1 5 5 4 5
## AMBI_W1_31 AMBI_W1_32 AMBI_W1_33 AMBI_W1_34 AMBI_W1_35 AMBI_W1_36
## 585 5 1 5 5 1 1
## AMBI_W1_37 AMBI_W1_38 AMBI_W1_39 AMBI_W1_40 AMBI_W1_41 AMBI_W1_42
## 585 5 1 4 2 5 5
## AMBI_W1_43 AMBI_W1_44 AMBI_W1_45 AMBI_W1_46 AMBI_W1_47 AMBI_W1_DO_1
## 585 5 5 5 2 2 15
## AMBI_W1_DO_2 AMBI_W1_DO_3 AMBI_W1_DO_4 AMBI_W1_DO_5 AMBI_W1_DO_6
## 585 26 22 20 44 42
## AMBI_W1_DO_7 AMBI_W1_DO_8 AMBI_W1_DO_9 AMBI_W1_DO_10 AMBI_W1_DO_11
## 585 2 13 10 46 28
## AMBI_W1_DO_12 AMBI_W1_DO_13 AMBI_W1_DO_14 AMBI_W1_DO_15 AMBI_W1_DO_16
## 585 9 12 43 32 3
## AMBI_W1_DO_17 AMBI_W1_DO_18 AMBI_W1_DO_19 AMBI_W1_DO_20 AMBI_W1_DO_21
## 585 41 11 16 30 17
## AMBI_W1_DO_22 AMBI_W1_DO_23 AMBI_W1_DO_24 AMBI_W1_DO_25 AMBI_W1_DO_26
## 585 45 18 35 36 19
## AMBI_W1_DO_27 AMBI_W1_DO_28 AMBI_W1_DO_29 AMBI_W1_DO_30 AMBI_W1_DO_31
## 585 29 38 4 31 40
## AMBI_W1_DO_32 AMBI_W1_DO_33 AMBI_W1_DO_34 AMBI_W1_DO_35 AMBI_W1_DO_36
## 585 47 1 27 37 7
## AMBI_W1_DO_37 AMBI_W1_DO_38 AMBI_W1_DO_39 AMBI_W1_DO_40 AMBI_W1_DO_41
## 585 21 25 34 8 23
## AMBI_W1_DO_42 AMBI_W1_DO_43 AMBI_W1_DO_44 AMBI_W1_DO_45 AMBI_W1_DO_46
## 585 6 5 33 39 14
## AMBI_W1_DO_47 V1.1_W_TIME_P1_First.Click V1.1_W_TIME_P1_Last.Click
## 585 24 NA NA
## V1.1_W_TIME_P1_Page.Submit V1.1_W_TIME_P1_Click.Count V1.1_W_JudgeOther_1
## 585 NA NA NA
## V1.1_W_JudgeOther_2 V1.1_W_JudgeOther_3 V1.1_W_JudgeOther_DO_1
## 585 NA NA NA
## V1.1_W_JudgeOther_DO_2 V1.1_W_JudgeOther_DO_3 V1.1_W_TIME_P2_First.Click
## 585 NA NA NA
## V1.1_W_TIME_P2_Last.Click V1.1_W_TIME_P2_Page.Submit
## 585 NA NA
## V1.1_W_TIME_P2_Click.Count V1.1_W_JudgeSelf_1 V1.1_W_JudgeSelf_2
## 585 NA NA NA
## V1.1_W_JudgeSelf_3 V1.1_W_JudgeSelf_DO_1 V1.1_W_JudgeSelf_DO_2
## 585 NA NA NA
## V1.1_W_JudgeSelf_DO_3 V1.2_TIME_P1_First.Click V1.2_TIME_P1_Last.Click
## 585 NA 5.829 12.738
## V1.2_TIME_P1_Page.Submit V1.2_TIME_P1_Click.Count V1.2_JudgeOther_1
## 585 13.878 6 0
## V1.2_JudgeOther_4 V1.2_JudgeOther_5 V1.2_JudgeOther_DO_1
## 585 0 0 3
## V1.2_JudgeOther_DO_4 V1.2_JudgeOther_DO_5 V1.2_TIME_P2_First.Click
## 585 1 2 1.778
## V1.2_TIME_P2_Last.Click V1.2_TIME_P2_Page.Submit V1.2_TIME_P2_Click.Count
## 585 27.38 28.469 7
## V1.2_JudgeSelf_1 V1.2_JudgeSelf_4 V1.2_JudgeSelf_5 V1.2_JudgeSelf_DO_1
## 585 47 48 0 1
## V1.2_JudgeSelf_DO_4 V1.2_JudgeSelf_DO_5 V1.1_B_TIME_P1_First.Click
## 585 3 2 8.285
## V1.1_B_TIME_P1_Last.Click V1.1_B_TIME_P1_Page.Submit
## 585 17.57 18.681
## V1.1_B_TIME_P1_Click.Count V1.1_B_JudgeOther_1 V1.1_B_JudgeOther_2
## 585 6 51 83
## V1.1_B_JudgeOther_3 V1.1_B_JudgeOther_DO_1 V1.1_B_JudgeOther_DO_2
## 585 0 2 3
## V1.1_B_JudgeOther_DO_3 V1.1_B_TIME_P2_First.Click V1.1_B_TIME_P2_Last.Click
## 585 1 16.182 24.196
## V1.1_B_TIME_P2_Page.Submit V1.1_B_TIME_P2_Click.Count V1.1_B_JudgeSelf_1
## 585 25.348 7 50
## V1.1_B_JudgeSelf_2 V1.1_B_JudgeSelf_3 V1.1_B_JudgeSelf_DO_1
## 585 49 0 2
## V1.1_B_JudgeSelf_DO_2 V1.1_B_JudgeSelf_DO_3 V1.1_I_TIME_P1_First.Click
## 585 1 3 NA
## V1.1_I_TIME_P1_Last.Click V1.1_I_TIME_P1_Page.Submit
## 585 NA NA
## V1.1_I_TIME_P1_Click.Count V1.1_I_JudgeOther_1 V1.1_I_JudgeOther_2
## 585 NA NA NA
## V1.1_I_JudgeOther_3 V1.1_I_JudgeOther_DO_1 V1.1_I_JudgeOther_DO_2
## 585 NA NA NA
## V1.1_I_JudgeOther_DO_3 V1.1_I_TIME_P2_First.Click V1.1_I_TIME_P2_Last.Click
## 585 NA NA NA
## V1.1_I_TIME_P2_Page.Submit V1.1_I_TIME_P2_Click.Count V1.1_I_JudgeSelf_1
## 585 NA NA NA
## V1.1_I_JudgeSelf_2 V1.1_I_JudgeSelf_3 V1.1_I_JudgeSelf_DO_1
## 585 NA NA NA
## V1.1_I_JudgeSelf_DO_2 V1.1_I_JudgeSelf_DO_3 V1.1_C_TIME_P1_First.Click
## 585 NA NA NA
## V1.1_C_TIME_P1_Last.Click V1.1_C_TIME_P1_Page.Submit
## 585 NA NA
## V1.1_C_TIME_P1_Click.Count V1.1_C_JudgeOther_1 V1.1_C_JudgeOther_2
## 585 NA NA NA
## V1.1_C_JudgeOther_3 V1.1_C_JudgeOther_DO_1 V1.1_C_JudgeOther_DO_2
## 585 NA NA NA
## V1.1_C_JudgeOther_DO_3 V1.1_C_TIME_P2_First.Click V1.1_C_TIME_P2_Last.Click
## 585 NA NA NA
## V1.1_C_TIME_P2_Page.Submit V1.1_C_TIME_P2_Click.Count V1.1_C_JudgeSelf_1
## 585 NA NA NA
## V1.1_C_JudgeSelf_2 V1.1_C_JudgeSelf_3 V1.1_C_JudgeSelf_DO_1
## 585 NA NA NA
## V1.1_C_JudgeSelf_DO_2 V1.1_C_JudgeSelf_DO_3 AMBI_W2_TIME_First.Click
## 585 NA NA 7.99
## AMBI_W2_TIME_Last.Click AMBI_W2_TIME_Page.Submit AMBI_W2_TIME_Click.Count
## 585 129.124 130.353 63
## AMBI_W2_1 AMBI_W2_2 AMBI_W2_3 AMBI_W2_4 AMBI_W2_5 AMBI_W2_6 AMBI_W2_7
## 585 1 4 5 1 1 5 1
## AMBI_W2_8 AMBI_W2_9 AMBI_W2_10 AMBI_W2_11 AMBI_W2_12 AMBI_W2_13 AMBI_W2_14
## 585 1 1 1 5 5 2 5
## AMBI_W2_15 AMBI_W2_16 AMBI_W2_17 AMBI_W2_18 AMBI_W2_19 AMBI_W2_20
## 585 5 1 5 4 5 4
## AMBI_W2_21 AMBI_W2_22 AMBI_W2_23 AMBI_W2_24 AMBI_W2_25 AMBI_W2_26
## 585 5 5 5 1 5 4
## AMBI_W2_27 AMBI_W2_28 AMBI_W2_29 AMBI_W2_30 AMBI_W2_31 AMBI_W2_32
## 585 1 1 1 1 5 5
## AMBI_W2_33 AMBI_W2_34 AMBI_W2_35 AMBI_W2_36 AMBI_W2_37 AMBI_W2_38
## 585 4 5 2 5 5 4
## AMBI_W2_39 AMBI_W2_40 AMBI_W2_41 AMBI_W2_42 AMBI_W2_43 AMBI_W2_44
## 585 1 1 2 2 1 1
## AMBI_W2_45 AMBI_W2_46 AMBI_W2_47 AMBI_W2_DO_1 AMBI_W2_DO_2 AMBI_W2_DO_3
## 585 1 5 5 26 2 38
## AMBI_W2_DO_4 AMBI_W2_DO_5 AMBI_W2_DO_6 AMBI_W2_DO_7 AMBI_W2_DO_8
## 585 36 13 19 6 40
## AMBI_W2_DO_9 AMBI_W2_DO_10 AMBI_W2_DO_11 AMBI_W2_DO_12 AMBI_W2_DO_13
## 585 14 27 8 12 30
## AMBI_W2_DO_14 AMBI_W2_DO_15 AMBI_W2_DO_16 AMBI_W2_DO_17 AMBI_W2_DO_18
## 585 32 24 10 47 18
## AMBI_W2_DO_19 AMBI_W2_DO_20 AMBI_W2_DO_21 AMBI_W2_DO_22 AMBI_W2_DO_23
## 585 35 33 44 3 29
## AMBI_W2_DO_24 AMBI_W2_DO_25 AMBI_W2_DO_26 AMBI_W2_DO_27 AMBI_W2_DO_28
## 585 43 37 7 21 45
## AMBI_W2_DO_29 AMBI_W2_DO_30 AMBI_W2_DO_31 AMBI_W2_DO_32 AMBI_W2_DO_33
## 585 22 25 41 16 31
## AMBI_W2_DO_34 AMBI_W2_DO_35 AMBI_W2_DO_36 AMBI_W2_DO_37 AMBI_W2_DO_38
## 585 1 34 39 46 4
## AMBI_W2_DO_39 AMBI_W2_DO_40 AMBI_W2_DO_41 AMBI_W2_DO_42 AMBI_W2_DO_43
## 585 5 28 17 15 11
## AMBI_W2_DO_44 AMBI_W2_DO_45 AMBI_W2_DO_46 AMBI_W2_DO_47
## 585 20 23 9 42
## V2.1_B_TIME_P1_First.Click V2.1_B_TIME_P1_Last.Click
## 585 NA NA
## V2.1_B_TIME_P1_Page.Submit V2.1_B_TIME_P1_Click.Count V2.1_B_JudgeOther_1
## 585 NA NA NA
## V2.1_B_JudgeOther_2 V2.1_B_JudgeOther_3 V2.1_B_JudgeOther_DO_1
## 585 NA NA NA
## V2.1_B_JudgeOther_DO_2 V2.1_B_JudgeOther_DO_3 V2.1_B_TIME_P2_First.Click
## 585 NA NA NA
## V2.1_B_TIME_P2_Last.Click V2.1_B_TIME_P2_Page.Submit
## 585 NA NA
## V2.1_B_TIME_P2_Click.Count V2.1_B_JudgeSelf_1 V2.1_B_JudgeSelf_2
## 585 NA NA NA
## V2.1_B_JudgeSelf_3 V2.1_B_JudgeSelf_DO_1 V2.1_B_JudgeSelf_DO_2
## 585 NA NA NA
## V2.1_B_JudgeSelf_DO_3 V2.2_TIME_P1_First.Click V2.2_TIME_P1_Last.Click
## 585 NA 5.287 13.407
## V2.2_TIME_P1_Page.Submit V2.2_TIME_P1_Click.Count V2.2_JudgeOther_1
## 585 14.497 6 51
## V2.2_JudgeOther_4 V2.2_JudgeOther_5 V2.2_JudgeOther_DO_1
## 585 51 92 2
## V2.2_JudgeOther_DO_4 V2.2_JudgeOther_DO_5 V2.2_TIME_P2_First.Click
## 585 1 3 3.32
## V2.2_TIME_P2_Last.Click V2.2_TIME_P2_Page.Submit V2.2_TIME_P2_Click.Count
## 585 10.662 11.586 5
## V2.2_JudgeSelf_1 V2.2_JudgeSelf_4 V2.2_JudgeSelf_5 V2.2_JudgeSelf_DO_1
## 585 53 100 99 1
## V2.2_JudgeSelf_DO_4 V2.2_JudgeSelf_DO_5 V2.1_W_TIME_P1_First.Click
## 585 3 2 6.397
## V2.1_W_TIME_P1_Last.Click V2.1_W_TIME_P1_Page.Submit
## 585 15.027 16.176
## V2.1_W_TIME_P1_Click.Count V2.1_W_JudgeOther_1 V2.1_W_JudgeOther_2
## 585 5 78 77
## V2.1_W_JudgeOther_3 V2.1_W_JudgeOther_DO_1 V2.1_W_JudgeOther_DO_2
## 585 100 2 1
## V2.1_W_JudgeOther_DO_3 V2.1_W_TIME_P2_First.Click V2.1_W_TIME_P2_Last.Click
## 585 3 19.874 32.168
## V2.1_W_TIME_P2_Page.Submit V2.1_W_TIME_P2_Click.Count V2.1_W_JudgeSelf_1
## 585 33.487 5 100
## V2.1_W_JudgeSelf_2 V2.1_W_JudgeSelf_3 V2.1_W_JudgeSelf_DO_1
## 585 100 100 3
## V2.1_W_JudgeSelf_DO_2 V2.1_W_JudgeSelf_DO_3 V2.1_C_TIME_P1_First.Click
## 585 2 1 NA
## V2.1_C_TIME_P1_Last.Click V2.1_C_TIME_P1_Page.Submit
## 585 NA NA
## V2.1_C_TIME_P1_Click.Count V2.1_C_JudgeOther_1 V2.1_C_JudgeOther_2
## 585 NA NA NA
## V2.1_C_JudgeOther_3 V2.1_C_JudgeOther_DO_1 V2.1_C_JudgeOther_DO_2
## 585 NA NA NA
## V2.1_C_JudgeOther_DO_3 V2.1_C_TIME_P2_First.Click V2.1_C_TIME_P2_Last.Click
## 585 NA NA NA
## V2.1_C_TIME_P2_Page.Submit V2.1_C_TIME_P2_Click.Count V2.1_C_JudgeSelf_1
## 585 NA NA NA
## V2.1_C_JudgeSelf_2 V2.1_C_JudgeSelf_3 V2.1_C_JudgeSelf_DO_1
## 585 NA NA NA
## V2.1_C_JudgeSelf_DO_2 V2.1_C_JudgeSelf_DO_3 V2.1_I_TIME_P1_First.Click
## 585 NA NA NA
## V2.1_I_TIME_P1_Last.Click V2.1_I_TIME_P1_Page.Submit
## 585 NA NA
## V2.1_I_TIME_P1_Click.Count V2.1_I_JudgeOther_1 V2.1_I_JudgeOther_2
## 585 NA NA NA
## V2.1_I_JudgeOther_3 V2.1_I_JudgeOther_DO_1 V2.1_I_JudgeOther_DO_2
## 585 NA NA NA
## V2.1_I_JudgeOther_DO_3 V2.1_I_TIME_P2_First.Click V2.1_I_TIME_P2_Last.Click
## 585 NA NA NA
## V2.1_I_TIME_P2_Page.Submit V2.1_I_TIME_P2_Click.Count V2.1_I_JudgeSelf_1
## 585 NA NA NA
## V2.1_I_JudgeSelf_2 V2.1_I_JudgeSelf_3 V2.1_I_JudgeSelf_DO_1
## 585 NA NA NA
## V2.1_I_JudgeSelf_DO_2 V2.1_I_JudgeSelf_DO_3 AMBI_W3_TIME_First.Click
## 585 NA NA 4.689
## AMBI_W3_TIME_Last.Click AMBI_W3_TIME_Page.Submit AMBI_W3_TIME_Click.Count
## 585 188.836 189.996 58
## AMBI_W3_1 AMBI_W3_2 AMBI_W3_3 AMBI_W3_4 AMBI_W3_5 AMBI_W3_6 AMBI_W3_7
## 585 1 3 5 4 4 1 5
## AMBI_W3_8 AMBI_W3_9 AMBI_W3_10 AMBI_W3_11 AMBI_W3_12 AMBI_W3_13 AMBI_W3_14
## 585 1 3 1 4 1 5 5
## AMBI_W3_15 AMBI_W3_16 AMBI_W3_17 AMBI_W3_18 AMBI_W3_19 AMBI_W3_20
## 585 1 1 4 2 1 5
## AMBI_W3_21 AMBI_W3_22 AMBI_W3_23 AMBI_W3_24 AMBI_W3_25 AMBI_W3_26
## 585 4 5 5 4 1 5
## AMBI_W3_27 AMBI_W3_28 AMBI_W3_29 AMBI_W3_30 AMBI_W3_31 AMBI_W3_32
## 585 2 5 1 1 1 4
## AMBI_W3_33 AMBI_W3_34 AMBI_W3_35 AMBI_W3_36 AMBI_W3_37 AMBI_W3_38
## 585 5 5 2 5 1 4
## AMBI_W3_39 AMBI_W3_40 AMBI_W3_41 AMBI_W3_42 AMBI_W3_43 AMBI_W3_44
## 585 3 5 1 5 5 1
## AMBI_W3_45 AMBI_W3_46 AMBI_W3_47 AMBI_W3_DO_1 AMBI_W3_DO_2 AMBI_W3_DO_3
## 585 5 5 3 39 47 34
## AMBI_W3_DO_4 AMBI_W3_DO_5 AMBI_W3_DO_6 AMBI_W3_DO_7 AMBI_W3_DO_8
## 585 14 35 13 7 11
## AMBI_W3_DO_9 AMBI_W3_DO_10 AMBI_W3_DO_11 AMBI_W3_DO_12 AMBI_W3_DO_13
## 585 22 20 31 26 17
## AMBI_W3_DO_14 AMBI_W3_DO_15 AMBI_W3_DO_16 AMBI_W3_DO_17 AMBI_W3_DO_18
## 585 41 2 37 8 19
## AMBI_W3_DO_19 AMBI_W3_DO_20 AMBI_W3_DO_21 AMBI_W3_DO_22 AMBI_W3_DO_23
## 585 24 3 5 44 30
## AMBI_W3_DO_24 AMBI_W3_DO_25 AMBI_W3_DO_26 AMBI_W3_DO_27 AMBI_W3_DO_28
## 585 45 29 38 10 32
## AMBI_W3_DO_29 AMBI_W3_DO_30 AMBI_W3_DO_31 AMBI_W3_DO_32 AMBI_W3_DO_33
## 585 6 25 46 18 15
## AMBI_W3_DO_34 AMBI_W3_DO_35 AMBI_W3_DO_36 AMBI_W3_DO_37 AMBI_W3_DO_38
## 585 16 43 21 36 27
## AMBI_W3_DO_39 AMBI_W3_DO_40 AMBI_W3_DO_41 AMBI_W3_DO_42 AMBI_W3_DO_43
## 585 42 28 23 12 40
## AMBI_W3_DO_44 AMBI_W3_DO_45 AMBI_W3_DO_46 AMBI_W3_DO_47
## 585 33 9 4 1
## V3.1_I_TIME_P1_First.Click V3.1_I_TIME_P1_Last.Click
## 585 NA NA
## V3.1_I_TIME_P1_Page.Submit V3.1_I_TIME_P1_Click.Count V3.1_I_JudgeOther_1
## 585 NA NA NA
## V3.1_I_JudgeOther_2 V3.1_I_JudgeOther_3 V3.1_I_JudgeOther_DO_1
## 585 NA NA NA
## V3.1_I_JudgeOther_DO_2 V3.1_I_JudgeOther_DO_3 V3.1_I_TIME_P2_First.Click
## 585 NA NA NA
## V3.1_I_TIME_P2_Last.Click V3.1_I_TIME_P2_Page.Submit
## 585 NA NA
## V3.1_I_TIME_P2_Click.Count V3.1_I_JudgeSelf_1 V3.1_I_JudgeSelf_2
## 585 NA NA NA
## V3.1_I_JudgeSelf_3 V3.1_I_JudgeSelf_DO_1 V3.1_I_JudgeSelf_DO_2
## 585 NA NA NA
## V3.1_I_JudgeSelf_DO_3 V3.2_TIME_P1_First.Click V3.2_TIME_P1_Last.Click
## 585 NA 4.523 13.18
## V3.2_TIME_P1_Page.Submit V3.2_TIME_P1_Click.Count V3.2_JudgeOther_1
## 585 14.294 6 54
## V3.2_JudgeOther_4 V3.2_JudgeOther_5 V3.2_JudgeOther_DO_1
## 585 56 96 1
## V3.2_JudgeOther_DO_4 V3.2_JudgeOther_DO_5 V3.2_TIME_P2_First.Click
## 585 2 3 2.469
## V3.2_TIME_P2_Last.Click V3.2_TIME_P2_Page.Submit V3.2_TIME_P2_Click.Count
## 585 18.81 19.828 6
## V3.2_JudgeSelf_1 V3.2_JudgeSelf_4 V3.2_JudgeSelf_5 V3.2_JudgeSelf_DO_1
## 585 73 75 100 1
## V3.2_JudgeSelf_DO_4 V3.2_JudgeSelf_DO_5 V3.1_C_TIME_P1_First.Click
## 585 3 2 3.076
## V3.1_C_TIME_P1_Last.Click V3.1_C_TIME_P1_Page.Submit
## 585 105.407 106.231
## V3.1_C_TIME_P1_Click.Count V3.1_C_JudgeOther_1 V3.1_C_JudgeOther_2
## 585 7 51 51
## V3.1_C_JudgeOther_3 V3.1_C_JudgeOther_DO_1 V3.1_C_JudgeOther_DO_2
## 585 100 3 1
## V3.1_C_JudgeOther_DO_3 V3.1_C_TIME_P2_First.Click V3.1_C_TIME_P2_Last.Click
## 585 2 102.609 139.931
## V3.1_C_TIME_P2_Page.Submit V3.1_C_TIME_P2_Click.Count V3.1_C_JudgeSelf_1
## 585 140.865 6 51
## V3.1_C_JudgeSelf_2 V3.1_C_JudgeSelf_3 V3.1_C_JudgeSelf_DO_1
## 585 52 96 1
## V3.1_C_JudgeSelf_DO_2 V3.1_C_JudgeSelf_DO_3 V3.1_W_TIME_P1_First.Click
## 585 2 3 NA
## V3.1_W_TIME_P1_Last.Click V3.1_W_TIME_P1_Page.Submit
## 585 NA NA
## V3.1_W_TIME_P1_Click.Count V3.1_W_JudgeOther_1 V3.1_W_JudgeOther_2
## 585 NA NA NA
## V3.1_W_JudgeOther_3 V3.1_W_JudgeOther_DO_1 V3.1_W_JudgeOther_DO_2
## 585 NA NA NA
## V3.1_W_JudgeOther_DO_3 V3.1_W_TIME_P2_First.Click V3.1_W_TIME_P2_Last.Click
## 585 NA NA NA
## V3.1_W_TIME_P2_Page.Submit V3.1_W_TIME_P2_Click.Count V3.1_W_JudgeSelf_1
## 585 NA NA NA
## V3.1_W_JudgeSelf_2 V3.1_W_JudgeSelf_3 V3.1_W_JudgeSelf_DO_1
## 585 NA NA NA
## V3.1_W_JudgeSelf_DO_2 V3.1_W_JudgeSelf_DO_3 V3.1_B_TIME_P1_First.Click
## 585 NA NA NA
## V3.1_B_TIME_P1_Last.Click V3.1_B_TIME_P1_Page.Submit
## 585 NA NA
## V3.1_B_TIME_P1_Click.Count V3.1_B_JudgeOther_1 V3.1_B_JudgeOther_2
## 585 NA NA NA
## V3.1_B_JudgeOther_3 V3.1_B_JudgeOther_DO_1 V3.1_B_JudgeOther_DO_2
## 585 NA NA NA
## V3.1_B_JudgeOther_DO_3 V3.1_B_TIME_P2_First.Click V3.1_B_TIME_P2_Last.Click
## 585 NA NA NA
## V3.1_B_TIME_P2_Page.Submit V3.1_B_TIME_P2_Click.Count V3.1_B_JudgeSelf_1
## 585 NA NA NA
## V3.1_B_JudgeSelf_2 V3.1_B_JudgeSelf_3 V3.1_B_JudgeSelf_DO_1
## 585 NA NA NA
## V3.1_B_JudgeSelf_DO_2 V3.1_B_JudgeSelf_DO_3 AMBI_W4_TIME_First.Click
## 585 NA NA 6.724
## AMBI_W4_TIME_Last.Click AMBI_W4_TIME_Page.Submit AMBI_W4_TIME_Click.Count
## 585 121.019 122.085 57
## AMBI_W4_1 AMBI_W4_2 AMBI_W4_3 AMBI_W4_4 AMBI_W4_5 AMBI_W4_6 AMBI_W4_7
## 585 5 5 5 1 5 5 5
## AMBI_W4_8 AMBI_W4_9 AMBI_W4_10 AMBI_W4_11 AMBI_W4_12 AMBI_W4_13 AMBI_W4_14
## 585 1 5 5 1 5 5 5
## AMBI_W4_15 AMBI_W4_16 AMBI_W4_17 AMBI_W4_18 AMBI_W4_19 AMBI_W4_20
## 585 1 4 1 3 2 1
## AMBI_W4_21 AMBI_W4_22 AMBI_W4_23 AMBI_W4_24 AMBI_W4_25 AMBI_W4_26
## 585 1 1 5 5 5 4
## AMBI_W4_27 AMBI_W4_28 AMBI_W4_29 AMBI_W4_30 AMBI_W4_31 AMBI_W4_32
## 585 5 1 1 5 1 5
## AMBI_W4_33 AMBI_W4_34 AMBI_W4_35 AMBI_W4_36 AMBI_W4_37 AMBI_W4_38
## 585 5 5 1 5 5 1
## AMBI_W4_39 AMBI_W4_40 AMBI_W4_41 AMBI_W4_42 AMBI_W4_43 AMBI_W4_44
## 585 4 5 5 5 5 2
## AMBI_W4_45 AMBI_W4_46 AMBI_W4_47 AMBI_W4_DO_1 AMBI_W4_DO_2 AMBI_W4_DO_3
## 585 5 5 1 17 35 42
## AMBI_W4_DO_4 AMBI_W4_DO_5 AMBI_W4_DO_6 AMBI_W4_DO_7 AMBI_W4_DO_8
## 585 47 15 1 27 24
## AMBI_W4_DO_9 AMBI_W4_DO_10 AMBI_W4_DO_11 AMBI_W4_DO_12 AMBI_W4_DO_13
## 585 16 11 10 19 2
## AMBI_W4_DO_14 AMBI_W4_DO_15 AMBI_W4_DO_16 AMBI_W4_DO_17 AMBI_W4_DO_18
## 585 3 39 37 26 9
## AMBI_W4_DO_19 AMBI_W4_DO_20 AMBI_W4_DO_21 AMBI_W4_DO_22 AMBI_W4_DO_23
## 585 44 21 29 32 5
## AMBI_W4_DO_24 AMBI_W4_DO_25 AMBI_W4_DO_26 AMBI_W4_DO_27 AMBI_W4_DO_28
## 585 13 7 28 22 8
## AMBI_W4_DO_29 AMBI_W4_DO_30 AMBI_W4_DO_31 AMBI_W4_DO_32 AMBI_W4_DO_33
## 585 40 23 46 30 45
## AMBI_W4_DO_34 AMBI_W4_DO_35 AMBI_W4_DO_36 AMBI_W4_DO_37 AMBI_W4_DO_38
## 585 6 41 25 4 14
## AMBI_W4_DO_39 AMBI_W4_DO_40 AMBI_W4_DO_41 AMBI_W4_DO_42 AMBI_W4_DO_43
## 585 43 38 18 31 34
## AMBI_W4_DO_44 AMBI_W4_DO_45 AMBI_W4_DO_46 AMBI_W4_DO_47
## 585 33 36 12 20
## V4.1_C_TIME_P1_First.Click V4.1_C_TIME_P1_Last.Click
## 585 NA NA
## V4.1_C_TIME_P1_Page.Submit V4.1_C_TIME_P1_Click.Count V4.1_C_JudgeOther_1
## 585 NA NA NA
## V4.1_C_JudgeOther_2 V4.1_C_JudgeOther_3 V4.1_C_JudgeOther_DO_1
## 585 NA NA NA
## V4.1_C_JudgeOther_DO_2 V4.1_C_JudgeOther_DO_3 V4.1_C_TIME_P2_First.Click
## 585 NA NA NA
## V4.1_C_TIME_P2_Last.Click V4.1_C_TIME_P2_Page.Submit
## 585 NA NA
## V4.1_C_TIME_P2_Click.Count V4.1_C_JudgeSelf_1 V4.1_C_JudgeSelf_2
## 585 NA NA NA
## V4.1_C_JudgeSelf_3 V4.1_C_JudgeSelf_DO_1 V4.1_C_JudgeSelf_DO_2
## 585 NA NA NA
## V4.1_C_JudgeSelf_DO_3 V4.2_TIME_P1_First.Click V4.2_TIME_P1_Last.Click
## 585 NA 5.57 14.051
## V4.2_TIME_P1_Page.Submit V4.2_TIME_P1_Click.Count V4.2_JudgeOther_1
## 585 15.177 5 53
## V4.2_JudgeOther_4 V4.2_JudgeOther_5 V4.2_JudgeOther_DO_1
## 585 53 4 1
## V4.2_JudgeOther_DO_4 V4.2_JudgeOther_DO_5 V4.2_TIME_P2_First.Click
## 585 2 3 6.196
## V4.2_TIME_P2_Last.Click V4.2_TIME_P2_Page.Submit V4.2_TIME_P2_Click.Count
## 585 12.478 13.757 6
## V4.2_JudgeSelf_1 V4.2_JudgeSelf_4 V4.2_JudgeSelf_5 V4.2_JudgeSelf_DO_1
## 585 53 52 0 2
## V4.2_JudgeSelf_DO_4 V4.2_JudgeSelf_DO_5 V4.1_I_TIME_P1_First.Click
## 585 1 3 4.055
## V4.1_I_TIME_P1_Last.Click V4.1_I_TIME_P1_Page.Submit
## 585 15.433 16.413
## V4.1_I_TIME_P1_Click.Count V4.1_I_JudgeOther_1 V4.1_I_JudgeOther_2
## 585 7 100 100
## V4.1_I_JudgeOther_3 V4.1_I_JudgeOther_DO_1 V4.1_I_JudgeOther_DO_2
## 585 100 3 1
## V4.1_I_JudgeOther_DO_3 V4.1_I_TIME_P2_First.Click V4.1_I_TIME_P2_Last.Click
## 585 2 3.076 12.534
## V4.1_I_TIME_P2_Page.Submit V4.1_I_TIME_P2_Click.Count V4.1_I_JudgeSelf_1
## 585 13.48 6 100
## V4.1_I_JudgeSelf_2 V4.1_I_JudgeSelf_3 V4.1_I_JudgeSelf_DO_1
## 585 100 100 2
## V4.1_I_JudgeSelf_DO_2 V4.1_I_JudgeSelf_DO_3 V4.1_B_TIME_P1_First.Click
## 585 1 3 NA
## V4.1_B_TIME_P1_Last.Click V4.1_B_TIME_P1_Page.Submit
## 585 NA NA
## V4.1_B_TIME_P1_Click.Count V4.1_B_JudgeOther_1 V4.1_B_JudgeOther_2
## 585 NA NA NA
## V4.1_B_JudgeOther_3 V4.1_B_JudgeOther_DO_1 V4.1_B_JudgeOther_DO_2
## 585 NA NA NA
## V4.1_B_JudgeOther_DO_3 V4.1_B_TIME_P2_First.Click V4.1_B_TIME_P2_Last.Click
## 585 NA NA NA
## V4.1_B_TIME_P2_Page.Submit V4.1_B_TIME_P2_Click.Count V4.1_B_JudgeSelf_1
## 585 NA NA NA
## V4.1_B_JudgeSelf_2 V4.1_B_JudgeSelf_3 V4.1_B_JudgeSelf_DO_1
## 585 NA NA NA
## V4.1_B_JudgeSelf_DO_2 V4.1_B_JudgeSelf_DO_3 V4.1_W_TIME_P1_First.Click
## 585 NA NA NA
## V4.1_W_TIME_P1_Last.Click V4.1_W_TIME_P1_Page.Submit
## 585 NA NA
## V4.1_W_TIME_P1_Click.Count V4.1_W_JudgeOther_1 V4.1_W_JudgeOther_2
## 585 NA NA NA
## V4.1_W_JudgeOther_3 V4.1_W_JudgeOther_DO_1 V4.1_W_JudgeOther_DO_2
## 585 NA NA NA
## V4.1_W_JudgeOther_DO_3 V4.1_W_TIME_P2_First.Click V4.1_W_TIME_P2_Last.Click
## 585 NA NA NA
## V4.1_W_TIME_P2_Page.Submit V4.1_W_TIME_P2_Click.Count V4.1_W_JudgeSelf_1
## 585 NA NA NA
## V4.1_W_JudgeSelf_2 V4.1_W_JudgeSelf_3 V4.1_W_JudgeSelf_DO_1
## 585 NA NA NA
## V4.1_W_JudgeSelf_DO_2 V4.1_W_JudgeSelf_DO_3 CHECK1 CHECK1_DO_1 CHECK1_DO_2
## 585 NA NA 1 1 4
## CHECK1_DO_3 CHECK1_DO_4 CHECK1_DO_5 CHECK2 CHECK2_DO_1 CHECK2_DO_2
## 585 3 2 5 1 4 1
## CHECK2_DO_3 CHECK2_DO_4 CHECK3 CHECK3_DO_1 CHECK3_DO_2 CHECK3_DO_3
## 585 2 3 1 4 3 1
## CHECK3_DO_4 CHECK3_DO_5 DEB1 DEB2_1 DEB2_2
## 585 2 5 Our mental health during the pandemic 1 0
## DEB2_3 DEB2_4 DEB2_5 DEB2_6 DEB2_7 DEB2_8 DEB2_9 DEB2_10 DEB2_11 DEB2_12
## 585 1 0 0 0 0 1 1 0 1 0
## DEB2_12_TEXT DEB2_DO_1 DEB2_DO_2 DEB2_DO_3 DEB2_DO_4 DEB2_DO_5 DEB2_DO_6
## 585 -99 11 1 5 6 8 2
## DEB2_DO_7 DEB2_DO_8 DEB2_DO_9 DEB2_DO_10 DEB2_DO_11 DEB2_DO_12 DEB3_1
## 585 4 9 7 10 3 12 4
## DEB3_2 DEB3_3 DEB3_4 DEB3_5 DEB3_6 DEB3_7 DEB3_8 DEB3_9 DEB3_10 DEB3_11
## 585 NA 3 NA NA NA NA 5 1 NA 2
## DEB3_12 DEB3_12_TEXT
## 585 NA
## DEB4
## 585 The pandemic has made my mental health plummet and I have pretty much spiraled into depression. I yearn to commit suicide, but I know that it would only cause trauma to the ones I love. I'm torn, but I know I don't want to live anymore.
## Q_BallotBoxStuffing Q_RelevantIDDuplicate Q_RelevantIDDuplicateScore
## 585 NA NA NA
## Q_RelevantIDFraudScore PROLIFIC_PID V_EthnicityOrder V1_Age
## 585 NA 5de53996227eed0b8dd9c44d 2 32
## V2_Age V3_Age V4_Age V1_Location V2_Location V3_Location V4_Location
## 585 39 43 33 in the city in the city in the city nearby
## V1_StoreType V2_StoreType V3_StoreType V4_StoreType V1_Name
## 585 department store department store supermarket supermarket Demetria
## V2_Name V3_Name V4_Name
## 585 Pamela Na Madhu
## V1_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
## V2_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
## V3_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
## V4_Framing
## 585 They wave to you and say that they are buying a large amount of
## V_Pethnicity V_MainOrder V1_Product
## 585 CTBH1122 cigarettes
## V1_Presentation
## 585 they are stocking up for what they expect to be an anxiety-ridden couple of months
## V2_Product
## 585 toilet paper
## V2_Presentation
## 585 they are stocking up for what they expect to be an anxiety-ridden couple of months
## V3_Product V3_Presentation
## 585 baby formula they are out of formula and their baby cannot eat otherwise
## V4_Product
## 585 hardware supplies
## V4_Presentation
## 585 their house has poor insulation and their roof is leaking, which is not safe for their family who are spending all of their time indoors
## SurveyID OTHER_COUNTRIES.1_13_TEXT...Parent.Topics
## 585 SV_3pZs8qIp1fybqV7 NA
## OTHER_COUNTRIES.1_13_TEXT...Sentiment.Polarity
## 585 NA
## OTHER_COUNTRIES.1_13_TEXT...Sentiment.Score
## 585 NA
## OTHER_COUNTRIES.1_13_TEXT...Sentiment OTHER_COUNTRIES.1_13_TEXT...Topics
## 585 NA
## OTHER_COUNTRIES.1_13_TEXT...Topic.Sentiment.Label
## 585 NA
## OTHER_COUNTRIES.1_13_TEXT...Topic.Sentiment.Score FL_172_DO_FL_173
## 585 NA NA
## FL_172_DO_FL_268 FL_172_DO_FL_267 FL_172_DO_FL_266 FL_172_DO_FL_265
## 585 NA NA NA NA
## FL_172_DO_FL_264 FL_172_DO_FL_263 FL_172_DO_FL_262 FL_172_DO_FL_261
## 585 NA NA NA NA
## FL_172_DO_FL_260 FL_172_DO_FL_259 FL_172_DO_FL_258 FL_172_DO_FL_257
## 585 NA NA NA NA
## FL_172_DO_FL_256 FL_172_DO_FL_255 FL_172_DO_FL_254 FL_172_DO_FL_253
## 585 NA NA NA NA
## FL_172_DO_FL_252 FL_172_DO_FL_251 FL_172_DO_FL_250 FL_172_DO_FL_249
## 585 NA NA NA NA
## FL_172_DO_FL_248 FL_172_DO_FL_247 FL_172_DO_FL_246 FL_172_DO_FL_245
## 585 NA NA NA NA
## FL_172_DO_FL_244 FL_172_DO_FL_243 FL_172_DO_FL_242 FL_172_DO_FL_241
## 585 NA NA NA NA
## FL_172_DO_FL_240 FL_172_DO_FL_239 FL_172_DO_FL_238 FL_172_DO_FL_237
## 585 NA NA NA NA
## FL_172_DO_FL_236 FL_172_DO_FL_235 FL_172_DO_FL_234 FL_172_DO_FL_233
## 585 NA NA NA NA
## FL_172_DO_FL_232 FL_172_DO_FL_231 FL_172_DO_FL_230 FL_172_DO_FL_229
## 585 NA NA NA NA
## FL_172_DO_FL_228 FL_172_DO_FL_227 FL_172_DO_FL_226 FL_172_DO_FL_225
## 585 NA NA NA NA
## FL_172_DO_FL_224 FL_172_DO_FL_223 FL_172_DO_FL_222 FL_172_DO_FL_221
## 585 NA NA NA NA
## FL_172_DO_FL_220 FL_172_DO_FL_219 FL_172_DO_FL_218 FL_172_DO_FL_217
## 585 NA NA NA NA
## FL_172_DO_FL_216 FL_172_DO_FL_215 FL_172_DO_FL_214 FL_172_DO_FL_213
## 585 NA NA NA NA
## FL_172_DO_FL_212 FL_172_DO_FL_211 FL_172_DO_FL_210 FL_172_DO_FL_209
## 585 NA NA NA NA
## FL_172_DO_FL_208 FL_172_DO_FL_207 FL_172_DO_FL_206 FL_172_DO_FL_205
## 585 NA NA NA NA
## FL_172_DO_FL_204 FL_172_DO_FL_203 FL_172_DO_FL_202 FL_172_DO_FL_201
## 585 NA NA NA NA
## FL_172_DO_FL_200 FL_172_DO_FL_199 FL_172_DO_FL_198 FL_172_DO_FL_197
## 585 NA NA NA NA
## FL_172_DO_FL_196 FL_172_DO_FL_195 FL_172_DO_FL_194 FL_172_DO_FL_193
## 585 NA NA NA NA
## FL_172_DO_FL_192 FL_172_DO_FL_191 FL_172_DO_FL_190 FL_172_DO_FL_189
## 585 NA NA NA NA
## FL_172_DO_FL_188 FL_172_DO_FL_187 FL_172_DO_FL_186 FL_172_DO_FL_185
## 585 NA NA NA 1
## FL_172_DO_FL_184 FL_172_DO_FL_183 FL_172_DO_FL_182 FL_172_DO_FL_181
## 585 NA NA NA NA
## FL_172_DO_FL_180 FL_172_DO_FL_179 FL_172_DO_FL_178 FL_172_DO_FL_177
## 585 NA NA NA NA
## FL_172_DO_FL_176 FL_172_DO_FL_175 FL_172_DO_FL_174 EXPGRP_TEXT
## 585 NA NA NA White
## CHIN_SPECIFIC_TEXT CONTINENT_BORN_TEXT_1 CONTINENT_BORN_TEXT_2
## 585 <NA> USA USA
## CONTINENT_BORN_TEXT_3 HAS_LIVED_USA HH_INCOME_TEXT DOB_YEAR
## 585 USA TRUE $10,000 to $30,000 1989
## DOB_YEAR_PERIODE EDUCATION_1_TEXT SEX_TEXT demo_class X.2 AMBI_1
## 585 (1985,1995] Associate degree in college Female 1 585 1
## AMBI_2 AMBI_3 AMBI_4 AMBI_5 AMBI_6 AMBI_7 AMBI_8 AMBI_9 AMBI_10 AMBI_11
## 585 1 1 1 1 1 1 4 3 4 1
## AMBI_12 AMBI_13 AMBI_14 AMBI_15 AMBI_16 AMBI_17 AMBI_18 AMBI_19 AMBI_20
## 585 5 5 5 1 1 1 1 5 1
## AMBI_21 AMBI_22 AMBI_23 AMBI_24 AMBI_25 AMBI_26 AMBI_27 AMBI_28 AMBI_29
## 585 5 1 4 5 5 1 5 5 4
## AMBI_30 AMBI_31 AMBI_32 AMBI_33 AMBI_34 AMBI_35 AMBI_36 AMBI_37 AMBI_38
## 585 5 5 1 5 5 1 1 5 1
## AMBI_39 AMBI_40 AMBI_41 AMBI_42 AMBI_43 AMBI_44 AMBI_45 AMBI_46 AMBI_47
## 585 4 2 5 5 5 5 5 2 1
## AMBI_48 AMBI_49 AMBI_50 AMBI_51 AMBI_52 AMBI_53 AMBI_54 AMBI_55 AMBI_56
## 585 4 5 1 1 5 1 1 1 1
## AMBI_57 AMBI_58 AMBI_59 AMBI_60 AMBI_61 AMBI_62 AMBI_63 AMBI_64 AMBI_65
## 585 5 5 2 5 5 1 5 4 5
## AMBI_66 AMBI_67 AMBI_68 AMBI_69 AMBI_70 AMBI_71 AMBI_72 AMBI_73 AMBI_74
## 585 4 5 5 5 1 5 4 1 1
## AMBI_75 AMBI_76 AMBI_77 AMBI_78 AMBI_79 AMBI_80 AMBI_81 AMBI_82 AMBI_83
## 585 5 5 5 2 5 5 4 1 1
## AMBI_84 AMBI_85 AMBI_86 AMBI_87 AMBI_88 AMBI_89 AMBI_90 AMBI_91 AMBI_92
## 585 2 2 1 1 1 5 1 3 5
## AMBI_93 AMBI_94 AMBI_95 AMBI_96 AMBI_97 AMBI_98 AMBI_99 AMBI_100 AMBI_101
## 585 4 4 1 5 1 3 1 4 1
## AMBI_102 AMBI_103 AMBI_104 AMBI_105 AMBI_106 AMBI_107 AMBI_108 AMBI_109
## 585 5 5 1 1 4 2 1 5
## AMBI_110 AMBI_111 AMBI_112 AMBI_113 AMBI_114 AMBI_115 AMBI_116 AMBI_117
## 585 4 5 5 4 1 5 2 5
## AMBI_118 AMBI_119 AMBI_120 AMBI_121 AMBI_122 AMBI_123 AMBI_124 AMBI_125
## 585 1 1 1 4 5 5 2 5
## AMBI_126 AMBI_127 AMBI_128 AMBI_129 AMBI_130 AMBI_131 AMBI_132 AMBI_133
## 585 1 4 3 5 1 5 5 1
## AMBI_134 AMBI_135 AMBI_136 AMBI_137 AMBI_138 AMBI_139 AMBI_140 AMBI_141
## 585 5 5 5 5 5 1 5 5
## AMBI_142 AMBI_143 AMBI_144 AMBI_145 AMBI_146 AMBI_147 AMBI_148 AMBI_149
## 585 5 1 5 5 1 5 5 5
## AMBI_150 AMBI_151 AMBI_152 AMBI_153 AMBI_154 AMBI_155 AMBI_156 AMBI_157
## 585 1 4 1 3 2 1 1 1
## AMBI_158 AMBI_159 AMBI_160 AMBI_161 AMBI_162 AMBI_163 AMBI_164 AMBI_165
## 585 5 5 5 4 5 1 1 5
## AMBI_166 AMBI_167 AMBI_168 AMBI_169 AMBI_170 AMBI_171 AMBI_172 AMBI_173
## 585 1 5 5 5 1 5 5 1
## AMBI_174 AMBI_175 AMBI_176 AMBI_177 AMBI_178 AMBI_179 AMBI_180 AMBI_181
## 585 4 5 5 5 5 2 5 5
## AMBI_MSR_1_NEOPIR_ANXIETY AMBI_MSR_2_NEOPIR_ANGRYHOSTILITY
## 585 0.8 0.8
## AMBI_MSR_3_NEOPIR_DEPRESSION AMBI_MSR_4_NEOPIR_SELFCONSCIOUSNESS
## 585 0.76 0.76
## AMBI_MSR_5_NEOPIR_IMPULSIVENESS AMBI_MSR_6_NEOPIR_VULNERABILITY
## 585 0.48 0.76
## AMBI_MSR_7_NEOPIR_WARMTH AMBI_MSR_8_NEOPIR_GREGARIOUSNESS
## 585 0 0
## AMBI_MSR_9_NEOPIR_ASSERTIVENESS AMBI_MSR_10_NEOPIR_ACTIVITY
## 585 0 0
## AMBI_MSR_11_NEOPIR_EXCITEMENTSEEKING AMBI_MSR_12_NEOPIR_POSITIVEEMOTIONS
## 585 0.32 0
## AMBI_MSR_13_NEOPIR_FANTASY AMBI_MSR_14_NEOPIR_AESTHETICS
## 585 0.6 0.8
## AMBI_MSR_15_NEOPIR_FEELINGS AMBI_MSR_16_NEOPIR_ACTIONS
## 585 0.64 0.44
## AMBI_MSR_17_NEOPIR_IDEAS AMBI_MSR_18_NEOPIR_VALUES AMBI_MSR_19_NEOPIR_TRUST
## 585 0.52 0.68 0.04
## AMBI_MSR_20_NEOPIR_STRAIGHTFORWARDNESS AMBI_MSR_21_NEOPIR_ALTRUISM
## 585 0.52 0.28
## AMBI_MSR_22_NEOPIR_COMPLIANCE AMBI_MSR_23_NEOPIR_MODESTY
## 585 0 0.8
## AMBI_MSR_24_NEOPIR_TENDERMINDEDNESS AMBI_MSR_25_NEOPIR_COMPETENCE
## 585 0.64 0.44
## AMBI_MSR_26_NEOPIR_ORDER AMBI_MSR_27_NEOPIR_DUTIFULNESS
## 585 0.8 0.48
## AMBI_MSR_28_NEOPIR_ACHIEVEMENTSTRIVING AMBI_MSR_29_NEOPIR_SELFDISCIPLINE
## 585 0.44 0.72
## AMBI_MSR_30_NEOPIR_DELIBERATION AMBI_MSR_31_HEXACOPI_SINCERITY
## 585 0.64 0.68
## AMBI_MSR_32_HEXACOPI_FAIRNESS AMBI_MSR_33_HEXACOPI_GREEDAVOIDANCE
## 585 0.44 0.76
## AMBI_MSR_34_HEXACOPI_MODESTY AMBI_MSR_35_HEXACOPI_FEARFULNESS
## 585 0.76 0.64
## AMBI_MSR_36_HEXACOPI_ANXIETY AMBI_MSR_37_HEXACOPI_DEPENDENCE
## 585 0.8 0.2
## AMBI_MSR_38_HEXACOPI_SENTIMENTALITY AMBI_MSR_39_HEXACOPI_EXPRESSIVENESS
## 585 0.64 0.08
## AMBI_MSR_40_HEXACOPI_SOCIALBOLDNESS AMBI_MSR_41_HEXACOPI_SOCIABILITY
## 585 0 0
## AMBI_MSR_42_HEXACOPI_LIVELINESS AMBI_MSR_43_HEXACOPI_FORGIVENESS
## 585 0 0.16
## AMBI_MSR_44_HEXACOPI_GENTLENESS AMBI_MSR_45_HEXACOPI_FLEXIBILITY
## 585 0.24 0.28
## AMBI_MSR_46_HEXACOPI_PATIENCE AMBI_MSR_47_HEXACOPI_ORGANIZATION
## 585 0.12 0.76
## AMBI_MSR_48_HEXACOPI_DILIGENCE AMBI_MSR_49_HEXACOPI_PERFECTIONISM
## 585 0.68 0.76
## AMBI_MSR_50_HEXACOPI_PRUDENCE AMBI_MSR_51_HEXACOPI_AESTHETICAPPRECIATION
## 585 0.8 0.8
## AMBI_MSR_52_HEXACOPI_INQUISITIVENESS AMBI_MSR_53_HEXACOPI_CREATIVITY
## 585 0.8 0.48
## AMBI_MSR_54_HEXACOPI_UNCONVENTIONALITY AMBI_MSR_55_JPIR_COMPLEXITY
## 585 0.68 0.64
## AMBI_MSR_56_JPIR_BREADTHOFINTEREST AMBI_MSR_57_JPIR_INNOVATION
## 585 0.8 0.44
## AMBI_MSR_58_JPIR_TOLERANCE AMBI_MSR_59_JPIR_EMPATHY
## 585 0.16 0.64
## AMBI_MSR_60_JPIR_ANXIETY AMBI_MSR_61_JPIR_COOPERATIVENESS
## 585 0.8 0.6
## AMBI_MSR_62_JPIR_SOCIABILITY AMBI_MSR_63_JPIR_SOCIALCONFIDENCE
## 585 0.04 0
## AMBI_MSR_64_JPIR_ENERGYLEVEL AMBI_MSR_65_JPIR_SOCIALASTUTENESS
## 585 0.16 0.28
## AMBI_MSR_66_JPIR_RISKTAKING AMBI_MSR_67_JPIR_ORGANIZATION
## 585 0.2 0.72
## AMBI_MSR_68_JPIR_TRADITIONALVALUES AMBI_MSR_69_JPIR_RESPONSIBILITY
## 585 0 0.44
## AMBI_MSR_70_MPQ_WELLBEING AMBI_MSR_71_MPQ_SOCIALPOTENCY
## 585 0 0
## AMBI_MSR_72_MPQ_ACHIEVEMENT AMBI_MSR_73_MPQ_SOCIALCLOSENESS
## 585 0.72 0.04
## AMBI_MSR_74_MPQ_STRESSREACTION AMBI_MSR_75_MPQ_AGGRESSION
## 585 0.8 0.48
## AMBI_MSR_76_MPQ_ALIENATION AMBI_MSR_77_MPQ_CONTROL
## 585 0.8 0.8
## AMBI_MSR_78_MPQ_HARMAVOIDANCE AMBI_MSR_79_MPQ_TRADITIONALISM
## 585 0.64 0.32
## AMBI_MSR_80_MPQ_ABSORPTION AMBI_MSR_81_6FPQ_AFFILIATION
## 585 0.48 0
## AMBI_MSR_82_6FPQ_DOMINANCE AMBI_MSR_83_6FPQ_EXHIBITION
## 585 0 0
## AMBI_MSR_84_6FPQ_ABASEMENT AMBI_MSR_85_6FPQ_EVENTEMPERED
## 585 0.08 0
## AMBI_MSR_86_6FPQ_GOODNATURED AMBI_MSR_87_6FPQ_COGNITIVESTRUCTURE
## 585 0.2 0.8
## AMBI_MSR_88_6FPQ_DELIBERATIVENESS AMBI_MSR_89_6FPQ_ORDER
## 585 0.8 0.76
## AMBI_MSR_90_6FPQ_AUTONOMY AMBI_MSR_91_6FPQ_INDIVIDUALISM
## 585 0.6 0.36
## AMBI_MSR_92_6FPQ_SELFRELIANCE AMBI_MSR_93_6FPQ_CHANGE
## 585 0.52 0.56
## AMBI_MSR_94_6FPQ_UNDERSTANDING AMBI_MSR_95_6FPQ_BREADTHOFINTEREST
## 585 0.8 0.8
## AMBI_MSR_96_6FPQ_ACHIEVEMENT AMBI_MSR_97_6FPQ_ENDURANCE
## 585 0.72 0.72
## AMBI_MSR_98_6FPQ_SERIOUSNESS AMBI_MSR_99_TCI_EXPLORATORYEXCITABILITY
## 585 0.24 0.56
## AMBI_MSR_100_TCI_IMPULSIVENESS AMBI_MSR_101_TCI_EXTRAVAGANCE
## 585 0 0.64
## AMBI_MSR_102_TCI_DISORDERLINESS AMBI_MSR_103_TCI_WORRY.PESSIMISM
## 585 0.28 0.8
## AMBI_MSR_104_TCI_FEAROFUNCERTAINTY AMBI_MSR_105_TCI_SHYNESSWITHSTRANGERS
## 585 0.64 0.8
## AMBI_MSR_106_TCI_FATIGABILITY.ASTHENIA AMBI_MSR_107_TCI_SENTIMENTALITY
## 585 0.48 0.6
## AMBI_MSR_108_TCI_WARMCOMMUNICATION AMBI_MSR_109_TCI_ATTACHMENT
## 585 0.12 0.16
## AMBI_MSR_110_TCI_DEPENDENCE AMBI_MSR_111_TCI_EAGERNESSOFEFFORT
## 585 0.48 0.56
## AMBI_MSR_112_TCI_WORKHARDENED AMBI_MSR_113_TCI_AMBITIOUS
## 585 0.68 0.32
## AMBI_MSR_114_TCI_PERFECTIONIST AMBI_MSR_115_TCI_RESPONSIBILITY
## 585 0.76 0
## AMBI_MSR_116_TCI_PURPOSEFULNESS AMBI_MSR_117_TCI_RESOURCEFULNESS
## 585 0 0.08
## AMBI_MSR_118_TCI_SELFACCEPTANCE AMBI_MSR_119_TCI_ENLIGHTENEDSECONDNATURE
## 585 0.4 0.16
## AMBI_MSR_120_TCI_SOCIALACCEPTANCE AMBI_MSR_121_TCI_EMPATHY
## 585 0.44 0.72
## AMBI_MSR_122_TCI_HELPFULNESS AMBI_MSR_123_TCI_COMPASSION
## 585 0.32 0
## AMBI_MSR_124_TCI_PUREHEARTEDCONSCIENCE AMBI_MSR_125_TCI_SELFFORGETFUL
## 585 0.24 0.48
## AMBI_MSR_126_TCI_TRANSPERSONALIDENTIFICATION
## 585 0.56
## AMBI_MSR_127_TCI_SPIRITUALACCEPTANCE AMBI_MSR_128_TCI_ENLIGHTENED
## 585 0 0
## AMBI_MSR_129_TCI_IDEALISTIC AMBI_MSR_130_CPI_DOMINANCE
## 585 0 0
## AMBI_MSR_131_CPI_CAPACITYFORSTATUS AMBI_MSR_132_CPI_SOCIABILITY
## 585 0.04 0
## AMBI_MSR_133_CPI_SOCIALPRESENCE AMBI_MSR_134_CPI_SELFACCEPTANCE
## 585 0.04 0
## AMBI_MSR_135_CPI_INDEPENDENCE AMBI_MSR_136_CPI_EMPATHY
## 585 0.04 0.04
## AMBI_MSR_137_CPI_RESPONSIBILITY AMBI_MSR_138_CPI_SOCIALIZATION
## 585 0.16 0.12
## AMBI_MSR_139_CPI_SELFCONTROL AMBI_MSR_140_CPI_GOODIMPRESSION
## 585 0.44 0
## AMBI_MSR_141_CPI_COMMUNALITY AMBI_MSR_142_CPI_WELLBEING
## 585 0.28 0
## AMBI_MSR_143_CPI_TOLERANCE AMBI_MSR_144_CPI_ACHIEVEMENTVIACONFORMANCE
## 585 0.04 0.16
## AMBI_MSR_145_CPI_ACHIEVEMENTVIAINDEPENDENCE
## 585 0.36
## AMBI_MSR_146_CPI_INTELLECTUALEFFICIENCY
## 585 0.32
## AMBI_MSR_147_CPI_PSYCHOLOGICALMINDEDNESS AMBI_MSR_148_CPI_FLEXIBILITY
## 585 0.32 0.12
## AMBI_MSR_149_CPI_FEMININITY AMBI_MSR_150_CPI_VECTOR1
## 585 0.68 0.8
## AMBI_MSR_151_CPI_VECTOR2 AMBI_MSR_152_CPI_VECTOR3
## 585 0.28 0.08
## AMBI_MSR_153_CPI_MANAGERIALPOTENTIAL AMBI_MSR_154_CPI_WORKORIENTATION
## 585 0 0
## AMBI_MSR_155_CPI_CREATIVETEMPERAMENT AMBI_MSR_156_CPI_LEADERSHIP
## 585 0.08 0.04
## AMBI_MSR_157_CPI_AMICABILITY AMBI_MSR_158_CPI_LAWENFORCEMENTORIENTATION
## 585 0 0.6
## AMBI_MSR_159_CPI_TOUGHMINDEDNESS AMBI_MSR_160_HPI_EMPATHY
## 585 0.04 0.12
## AMBI_MSR_161_HPI_NOTANXIOUS AMBI_MSR_162_HPI_NOGUILT
## 585 0 0
## AMBI_MSR_163_HPI_CALMNESS AMBI_MSR_164_HPI_EVENTEMPERED
## 585 0 0
## AMBI_MSR_165_HPI_NOSOMATICCOMPLAINTS AMBI_MSR_166_HPI_TRUSTING
## 585 0 0.04
## AMBI_MSR_167_HPI_GOODATTACHMENT AMBI_MSR_168_HPI_COMPETITIVE
## 585 0.04 0.16
## AMBI_MSR_169_HPI_SELFCONFIDENCE AMBI_MSR_170_HPI_NODEPRESSION
## 585 0.04 0
## AMBI_MSR_171_HPI_LEADERSHIP AMBI_MSR_172_HPI_IDENTITY
## 585 0 0.16
## AMBI_MSR_173_HPI_NOSOCIALANXIETY AMBI_MSR_174_HPI_LIKESPARTIES
## 585 0.04 0.16
## AMBI_MSR_175_HPI_LIKESCROWDS AMBI_MSR_176_HPI_EXPERIENCESEEKING
## 585 0.16 0.6
## AMBI_MSR_177_HPI_EXHIBITIONISTIC AMBI_MSR_178_HPI_ENTERTAINING
## 585 0.04 0.2
## AMBI_MSR_179_HPI_EASYTOLIVEWITH AMBI_MSR_180_HPI_SENSITIVE
## 585 0.12 0.72
## AMBI_MSR_181_HPI_CARING AMBI_MSR_182_HPI_LIKESPEOPLE
## 585 0.44 0
## AMBI_MSR_183_HPI_NOHOSTILITY AMBI_MSR_184_HPI_MORALISTIC
## 585 0.12 0.28
## AMBI_MSR_185_HPI_MASTERY AMBI_MSR_186_HPI_VIRTUOUS
## 585 0.76 0.16
## AMBI_MSR_187_HPI_NOTAUTONOMOUS AMBI_MSR_188_HPI_NOTSPONTANEOUS
## 585 0.6 0.36
## AMBI_MSR_189_HPI_IMPULSECONTROL AMBI_MSR_190_HPI_AVOIDSTROUBLE
## 585 0.6 0.12
## AMBI_MSR_191_HPI_SCIENCEABILITY AMBI_MSR_192_HPI_CURIOSITY
## 585 0.4 0.4
## AMBI_MSR_193_HPI_THRILLSEEKING AMBI_MSR_194_HPI_INTELLECTUALGAMES
## 585 0.48 0.6666667
## AMBI_MSR_195_HPI_GENERATESIDEAS AMBI_MSR_196_HPI_CULTURE
## 585 0.08 0.8
## AMBI_MSR_197_HPI_EDUCATION AMBI_MSR_198_HPI_MATHABILITY
## 585 0.44 0.4
## AMBI_MSR_199_HPI_GOODMEMORY AMBI_MSR_200_HPI_READING
## 585 0.6 0.76
## AMBI_MSR_201_HPI_SELFFOCUS AMBI_MSR_202_HPI_IMPRESSIONMANAGEMENT
## 585 0.64 0.64
## AMBI_MSR_203_HPI_APPEARANCE AMBI_MSR_EFA_ULS1 AMBI_MSR_EFA_ULS2
## 585 0.6 -0.0258397 -0.02557284
## AMBI_MSR_EFA_ULS3 AMBI_MSR_EFA_ULS5 AMBI_MSR_EFA_ULS4 AMBI_MSR_EFA_ULS6
## 585 0.009911969 -0.01754083 0.01474402 -0.01662301
## AMBI_MSR_EFA_ULS9 AMBI_MSR_EFA_ULS8 AMBI_MSR_EFA_ULS13 AMBI_MSR_EFA_ULS10
## 585 -0.00153324 0.0009325445 0.007788777 -0.00716766
## AMBI_MSR_EFA_ULS7 AMBI_MSR_EFA_ULS11 AMBI_MSR_EFA_ULS12
## 585 -0.01644725 0.005647905 -0.00138405
## AMBI_BIG5_Neuroticism AMBI_BIG5_Extraversion AMBI_BIG5_Openness
## 585 2.257805 -2.948208 0.798661
## AMBI_BIG5_Agreeableness AMBI_BIG5_Conscientiousness covmis_att_flu
## 585 -2.312 0.9416817 1
## covmis_att_afrDie covmis_att_eldrNoBgDl covmis_att_rareNoWorr
## 585 1 1 1
## covmis_att_bgThrt covmis_cnsp_ctiusAsian covmis_cnsp_stpCovStpImmi
## 585 1 1 1
## covmis_cnsp_redIntWthChina covmis_cnsp_chnsCovRcst covmis_orgn_covPlnnd
## 585 1 1 1
## covmis_orgn_covNat covmis_orgn_covNgeenLab covmis_orgn_scntFkNwsCov
## 585 1 1 1
## covmis_pltc_polBgDlIntrst covmis_pltc_covNtSerPolSay
## 585 1 1
## covmis_pltc_polDwnplCovPlpLDngr covmis_cvrg_mdiaCovBgrDl
## 585 1 1
## covmis_cvrg_nwsGdJbComCov covmis_cvrg_mdiaUseCovMkTrmpRepLkBd
## 585 2 1
## covmis_anti_frGovUseCovMndtVacc covmis_anti_thnksNoCovVacc
## 585 1 1
## covmis_anti_covVacEffRedVirus covmis_mdsk_medOrgUntrust
## 585 1 1
## covmis_mdsk_skeptInfoDocSci covmis_mdsk_medOrgRecBstInt
## 585 1 1
## covmis_mdsk_fllwRecMedOrgImp cov_class covqual_class
## 585 1 1 1
## age_education neuroticism_qual extraversion_qual openness_qual
## 585 25 - 45y_College degree high <NA> high
## conscientiousness_qual agreeableness_qual covqual_class_2
## 585 high weak Believersdf_covmis2 <- df_covmis %>% filter((!is.na(CONTINENT_BORN_TEXT_1))) %>%
filter((!is.na(neuroticism_qual))) %>%
filter((!is.na(extraversion_qual))) %>%
filter((!is.na(openness_qual))) %>%
filter((!is.na(conscientiousness_qual))) %>%
filter((!is.na(agreeableness_qual)))
which(is.na(df_covmis2 %>% dplyr::select(EXPGRP_TEXT, CONTINENT_BORN_TEXT_1, HAS_LIVED_USA, SEX_TEXT,
age_education, neuroticism_qual, extraversion_qual, openness_qual,
conscientiousness_qual, agreeableness_qual)), arr.ind=TRUE)## row coldf_covmis$CONTINENT_BORN_TEXT_1 %>% levels()## [1] "USA" "4Tigers and Japan" "Africa"
## [4] "Central Eastern Europe" "Developping Asia" "Middle East"
## [7] "North America" "Oceania" "South America"
## [10] "Western Europe"df_covmis$CONTINENT_BORN_TEXT_1 %>% levels()## [1] "USA" "4Tigers and Japan" "Africa"
## [4] "Central Eastern Europe" "Developping Asia" "Middle East"
## [7] "North America" "Oceania" "South America"
## [10] "Western Europe"reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
age_education + neuroticism_qual + extraversion_qual + openness_qual +
conscientiousness_qual + agreeableness_qual,
data = df_covmis2,
family = binomial(logit))
step(reg)## Start: AIC=375.18
## covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA +
## SEX_TEXT + age_education + neuroticism_qual + extraversion_qual +
## openness_qual + conscientiousness_qual + agreeableness_qual
##
## Df Deviance AIC
## - age_education 8 315.04 367.04
## - CONTINENT_BORN_TEXT_1 9 318.14 368.14
## - SEX_TEXT 3 308.22 370.22
## - extraversion_qual 2 308.59 372.59
## - neuroticism_qual 2 308.70 372.70
## - HAS_LIVED_USA 1 307.47 373.47
## - agreeableness_qual 2 309.59 373.59
## - conscientiousness_qual 2 309.67 373.67
## <none> 307.18 375.18
## - openness_qual 2 313.51 377.51
## - EXPGRP_TEXT 2 324.23 388.23
##
## Step: AIC=367.04
## covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA +
## SEX_TEXT + neuroticism_qual + extraversion_qual + openness_qual +
## conscientiousness_qual + agreeableness_qual
##
## Df Deviance AIC
## - CONTINENT_BORN_TEXT_1 9 324.76 358.76
## - SEX_TEXT 3 316.05 362.05
## - extraversion_qual 2 315.84 363.84
## - neuroticism_qual 2 316.45 364.45
## - conscientiousness_qual 2 316.64 364.64
## - HAS_LIVED_USA 1 315.40 365.40
## - agreeableness_qual 2 317.77 365.77
## <none> 315.04 367.04
## - openness_qual 2 321.24 369.24
## - EXPGRP_TEXT 2 334.18 382.18
##
## Step: AIC=358.76
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + neuroticism_qual +
## extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual
##
## Df Deviance AIC
## - SEX_TEXT 3 325.72 353.72
## - extraversion_qual 2 325.51 355.51
## - neuroticism_qual 2 326.00 356.00
## - agreeableness_qual 2 326.48 356.48
## - conscientiousness_qual 2 326.52 356.52
## - HAS_LIVED_USA 1 326.14 358.14
## <none> 324.76 358.76
## - openness_qual 2 330.51 360.51
## - EXPGRP_TEXT 2 342.94 372.94
##
## Step: AIC=353.72
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual +
## extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual
##
## Df Deviance AIC
## - extraversion_qual 2 326.61 350.61
## - neuroticism_qual 2 327.04 351.04
## - agreeableness_qual 2 327.36 351.36
## - conscientiousness_qual 2 327.46 351.46
## - HAS_LIVED_USA 1 327.04 353.04
## <none> 325.72 353.72
## - openness_qual 2 331.60 355.60
## - EXPGRP_TEXT 2 343.90 367.90
##
## Step: AIC=350.61
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual +
## openness_qual + conscientiousness_qual + agreeableness_qual
##
## Df Deviance AIC
## - neuroticism_qual 2 328.17 348.17
## - agreeableness_qual 2 328.32 348.32
## - conscientiousness_qual 2 328.53 348.53
## - HAS_LIVED_USA 1 327.70 349.70
## <none> 326.61 350.61
## - openness_qual 2 332.32 352.32
## - EXPGRP_TEXT 2 344.18 364.18
##
## Step: AIC=348.17
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual +
## conscientiousness_qual + agreeableness_qual
##
## Df Deviance AIC
## - agreeableness_qual 2 330.29 346.29
## - conscientiousness_qual 2 330.57 346.57
## - HAS_LIVED_USA 1 329.10 347.10
## <none> 328.17 348.17
## - openness_qual 2 333.99 349.99
## - EXPGRP_TEXT 2 344.91 360.91
##
## Step: AIC=346.29
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual +
## conscientiousness_qual
##
## Df Deviance AIC
## - conscientiousness_qual 2 332.11 344.11
## - HAS_LIVED_USA 1 331.00 345.00
## <none> 330.29 346.29
## - openness_qual 2 336.76 348.76
## - EXPGRP_TEXT 2 346.38 358.38
##
## Step: AIC=344.11
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual
##
## Df Deviance AIC
## - HAS_LIVED_USA 1 333.39 343.39
## <none> 332.11 344.11
## - openness_qual 2 337.60 345.60
## - EXPGRP_TEXT 2 349.40 357.40
##
## Step: AIC=343.39
## covqual_class_2 ~ EXPGRP_TEXT + openness_qual
##
## Df Deviance AIC
## <none> 333.39 343.39
## - openness_qual 2 338.61 344.61
## - EXPGRP_TEXT 2 349.54 355.54##
## Call: glm(formula = covqual_class_2 ~ EXPGRP_TEXT + openness_qual,
## family = binomial(logit), data = df_covmis2)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian
## -3.0838 0.9851
## EXPGRP_TEXTWhite openness_qualmiddle
## 1.5320 -0.3869
## openness_qualhigh
## -1.0616
##
## Degrees of Freedom: 651 Total (i.e. Null); 647 Residual
## Null Deviance: 352.9
## Residual Deviance: 333.4 AIC: 343.4reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual,
data = df_covmis2,
family = binomial(logit))
ggcoef_model(reg, exponentiate = TRUE)
df_covmis2$EDUCATION_2_TEXT <- fct_recode(df_covmis2$EDUCATION_1 %>% as.character,
"No college degree"="1",
"No college degree"="2",
"No college degree"="3",
"College degree"="4",
"College degree"="5",
"Graduate degree"="6",
"Graduate degree"="7",
"Graduate degree"="8")
df_covmis2$DOB_AGE_BRACKET <- fct_recode(df_covmis2$DOB_YEAR_PERIODE %>% as.character,
"-25y"="(1995,2005]",
"25 - 45y"="(1985,1995]",
"25 - 45y"="(1975,1985]",
"+45y"="(1944,1955]",
"+45y"="(1955,1965]",
"+45y"="(1965,1975]")
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual +
conscientiousness_qual + agreeableness_qual,
data = df_covmis2)## # weights: 93 (60 variable)
## initial value 716.295212
## iter 10 value 462.188017
## iter 20 value 449.856776
## iter 30 value 447.444923
## iter 40 value 446.934218
## iter 50 value 446.724179
## iter 60 value 446.717265
## final value 446.717240
## convergedstep(regm)## Start: AIC=1013.43
## covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA +
## SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual +
## extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 472.822904
## iter 20 value 463.510030
## iter 30 value 461.876855
## iter 40 value 461.580380
## iter 50 value 461.455216
## iter 60 value 461.452175
## final value 461.452165
## converged
## trying - CONTINENT_BORN_TEXT_1
## # weights: 66 (42 variable)
## initial value 716.295212
## iter 10 value 476.303301
## iter 20 value 458.769836
## iter 30 value 457.522823
## iter 40 value 457.361151
## iter 50 value 457.346844
## final value 457.346788
## converged
## trying - HAS_LIVED_USA
## # weights: 90 (58 variable)
## initial value 716.295212
## iter 10 value 464.157904
## iter 20 value 450.973604
## iter 30 value 448.654999
## iter 40 value 448.105546
## iter 50 value 447.976268
## iter 60 value 447.972657
## iter 60 value 447.972654
## iter 60 value 447.972654
## final value 447.972654
## converged
## trying - SEX_TEXT
## # weights: 84 (54 variable)
## initial value 716.295212
## iter 10 value 463.994226
## iter 20 value 453.708227
## iter 30 value 451.022245
## iter 40 value 450.600171
## iter 50 value 450.456552
## final value 450.453910
## converged
## trying - EDUCATION_2_TEXT
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 464.349983
## iter 20 value 452.621073
## iter 30 value 449.748916
## iter 40 value 449.243133
## iter 50 value 449.064649
## iter 60 value 449.063044
## iter 60 value 449.063040
## iter 60 value 449.063040
## final value 449.063040
## converged
## trying - DOB_AGE_BRACKET
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 459.170523
## iter 20 value 450.172555
## iter 30 value 448.409313
## iter 40 value 448.115124
## iter 50 value 448.009338
## final value 448.008688
## converged
## trying - neuroticism_qual
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 463.735184
## iter 20 value 452.902780
## iter 30 value 450.779994
## iter 40 value 450.344219
## iter 50 value 450.241142
## final value 450.239877
## converged
## trying - extraversion_qual
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 461.369512
## iter 20 value 451.551182
## iter 30 value 449.765707
## iter 40 value 449.456948
## iter 50 value 449.312706
## final value 449.311605
## converged
## trying - openness_qual
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 474.441827
## iter 20 value 460.569784
## iter 30 value 458.684048
## iter 40 value 458.260373
## iter 50 value 458.071146
## iter 60 value 458.068207
## iter 60 value 458.068203
## iter 60 value 458.068203
## final value 458.068203
## converged
## trying - conscientiousness_qual
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 462.565089
## iter 20 value 450.285428
## iter 30 value 448.374425
## iter 40 value 448.041717
## iter 50 value 447.940450
## final value 447.939717
## converged
## trying - agreeableness_qual
## # weights: 87 (56 variable)
## initial value 716.295212
## iter 10 value 471.130934
## iter 20 value 460.062578
## iter 30 value 458.306717
## iter 40 value 457.932446
## iter 50 value 457.827799
## final value 457.826508
## converged
## Df AIC
## - CONTINENT_BORN_TEXT_1 42 998.6936
## - conscientiousness_qual 56 1007.8794
## - DOB_AGE_BRACKET 56 1008.0174
## - SEX_TEXT 54 1008.9078
## - EDUCATION_2_TEXT 56 1010.1261
## - extraversion_qual 56 1010.6232
## - HAS_LIVED_USA 58 1011.9453
## - neuroticism_qual 56 1012.4798
## <none> 60 1013.4345
## - agreeableness_qual 56 1027.6530
## - openness_qual 56 1028.1364
## - EXPGRP_TEXT 56 1034.9043
## # weights: 66 (42 variable)
## initial value 716.295212
## iter 10 value 476.303301
## iter 20 value 458.769836
## iter 30 value 457.522823
## iter 40 value 457.361151
## iter 50 value 457.346844
## final value 457.346788
## converged
##
## Step: AIC=998.69
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT +
## DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual +
## openness_qual + conscientiousness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 482.006351
## iter 20 value 474.009622
## iter 30 value 473.356680
## iter 40 value 473.223026
## iter 50 value 473.217076
## final value 473.217067
## converged
## trying - HAS_LIVED_USA
## # weights: 63 (40 variable)
## initial value 716.295212
## iter 10 value 469.183596
## iter 20 value 459.957524
## iter 30 value 459.581164
## iter 40 value 459.438291
## iter 50 value 459.432258
## final value 459.432248
## converged
## trying - SEX_TEXT
## # weights: 57 (36 variable)
## initial value 716.295212
## iter 10 value 473.679923
## iter 20 value 461.845410
## iter 30 value 461.558145
## final value 461.555558
## converged
## trying - EDUCATION_2_TEXT
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 473.796676
## iter 20 value 460.332798
## iter 30 value 459.729631
## iter 40 value 459.593057
## iter 50 value 459.590678
## iter 50 value 459.590675
## iter 50 value 459.590675
## final value 459.590675
## converged
## trying - DOB_AGE_BRACKET
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 466.226151
## iter 20 value 459.145396
## iter 30 value 458.695891
## iter 40 value 458.577337
## final value 458.576013
## converged
## trying - neuroticism_qual
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 473.522075
## iter 20 value 461.335725
## iter 30 value 460.831027
## iter 40 value 460.705076
## iter 50 value 460.702321
## final value 460.702315
## converged
## trying - extraversion_qual
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 469.906274
## iter 20 value 460.960143
## iter 30 value 460.370533
## iter 40 value 460.229655
## iter 50 value 460.225499
## iter 50 value 460.225495
## iter 50 value 460.225495
## final value 460.225495
## converged
## trying - openness_qual
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 479.863015
## iter 20 value 468.860657
## iter 30 value 468.492499
## iter 40 value 468.363461
## iter 50 value 468.359634
## iter 50 value 468.359630
## iter 50 value 468.359630
## final value 468.359630
## converged
## trying - conscientiousness_qual
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 468.075782
## iter 20 value 459.221133
## iter 30 value 458.604919
## iter 40 value 458.482645
## final value 458.480557
## converged
## trying - agreeableness_qual
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 475.102411
## iter 20 value 468.631555
## iter 30 value 468.284388
## iter 40 value 468.176880
## final value 468.173391
## converged
## Df AIC
## - conscientiousness_qual 38 992.9611
## - DOB_AGE_BRACKET 38 993.1520
## - SEX_TEXT 36 995.1111
## - EDUCATION_2_TEXT 38 995.1813
## - extraversion_qual 38 996.4510
## - neuroticism_qual 38 997.4046
## <none> 42 998.6936
## - HAS_LIVED_USA 40 998.8645
## - agreeableness_qual 38 1012.3468
## - openness_qual 38 1012.7193
## - EXPGRP_TEXT 38 1022.4341
## # weights: 60 (38 variable)
## initial value 716.295212
## iter 10 value 468.075782
## iter 20 value 459.221133
## iter 30 value 458.604919
## iter 40 value 458.482645
## final value 458.480557
## converged
##
## Step: AIC=992.96
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT +
## DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual +
## openness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 480.713401
## iter 20 value 475.574541
## iter 30 value 475.215263
## iter 40 value 475.117600
## final value 475.116554
## converged
## trying - HAS_LIVED_USA
## # weights: 57 (36 variable)
## initial value 716.295212
## iter 10 value 467.414353
## iter 20 value 461.171583
## iter 30 value 460.870733
## iter 40 value 460.800107
## final value 460.799382
## converged
## trying - SEX_TEXT
## # weights: 51 (32 variable)
## initial value 716.295212
## iter 10 value 469.313577
## iter 20 value 462.959865
## iter 30 value 462.867221
## final value 462.866384
## converged
## trying - EDUCATION_2_TEXT
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 467.076000
## iter 20 value 460.974808
## iter 30 value 460.611621
## iter 40 value 460.555945
## final value 460.555680
## converged
## trying - DOB_AGE_BRACKET
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 466.950035
## iter 20 value 460.136328
## iter 30 value 459.802518
## iter 40 value 459.742465
## final value 459.742009
## converged
## trying - neuroticism_qual
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 468.436171
## iter 20 value 462.311018
## iter 30 value 461.959446
## iter 40 value 461.905995
## final value 461.905669
## converged
## trying - extraversion_qual
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 467.590649
## iter 20 value 462.070815
## iter 30 value 461.706140
## iter 40 value 461.629205
## final value 461.628842
## converged
## trying - openness_qual
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 475.750647
## iter 20 value 469.702561
## iter 30 value 469.386659
## iter 40 value 469.285657
## final value 469.284755
## converged
## trying - agreeableness_qual
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 474.373455
## iter 20 value 469.811080
## iter 30 value 469.517946
## iter 40 value 469.454180
## final value 469.453810
## converged
## Df AIC
## - DOB_AGE_BRACKET 34 987.4840
## - EDUCATION_2_TEXT 34 989.1114
## - SEX_TEXT 32 989.7328
## - extraversion_qual 34 991.2577
## - neuroticism_qual 34 991.8113
## <none> 38 992.9611
## - HAS_LIVED_USA 36 993.5988
## - openness_qual 34 1006.5695
## - agreeableness_qual 34 1006.9076
## - EXPGRP_TEXT 34 1018.2331
## # weights: 54 (34 variable)
## initial value 716.295212
## iter 10 value 466.950035
## iter 20 value 460.136328
## iter 30 value 459.802518
## iter 40 value 459.742465
## final value 459.742009
## converged
##
## Step: AIC=987.48
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT +
## neuroticism_qual + extraversion_qual + openness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 480.866753
## iter 20 value 477.922202
## iter 30 value 477.654741
## iter 40 value 477.627859
## final value 477.627793
## converged
## trying - HAS_LIVED_USA
## # weights: 51 (32 variable)
## initial value 716.295212
## iter 10 value 468.175248
## iter 20 value 462.558182
## iter 30 value 462.297855
## iter 40 value 462.254846
## final value 462.254730
## converged
## trying - SEX_TEXT
## # weights: 45 (28 variable)
## initial value 716.295212
## iter 10 value 470.231811
## iter 20 value 463.989649
## iter 30 value 463.947544
## final value 463.947516
## converged
## trying - EDUCATION_2_TEXT
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 467.008671
## iter 20 value 462.452630
## iter 30 value 462.129610
## iter 40 value 462.118337
## final value 462.118296
## converged
## trying - neuroticism_qual
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 468.835352
## iter 20 value 463.787350
## iter 30 value 463.486672
## iter 40 value 463.476190
## final value 463.476157
## converged
## trying - extraversion_qual
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 468.344434
## iter 20 value 463.223946
## iter 30 value 462.894540
## iter 40 value 462.866913
## final value 462.866844
## converged
## trying - openness_qual
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 476.106103
## iter 20 value 470.788041
## iter 30 value 470.498705
## iter 40 value 470.459428
## final value 470.459334
## converged
## trying - agreeableness_qual
## # weights: 48 (30 variable)
## initial value 716.295212
## iter 10 value 477.094706
## iter 20 value 470.971046
## iter 30 value 470.684279
## iter 40 value 470.650118
## final value 470.650076
## converged
## Df AIC
## - SEX_TEXT 28 983.8950
## - EDUCATION_2_TEXT 30 984.2366
## - extraversion_qual 30 985.7337
## - neuroticism_qual 30 986.9523
## <none> 34 987.4840
## - HAS_LIVED_USA 32 988.5095
## - openness_qual 30 1000.9187
## - agreeableness_qual 30 1001.3002
## - EXPGRP_TEXT 30 1015.2556
## # weights: 45 (28 variable)
## initial value 716.295212
## iter 10 value 470.231811
## iter 20 value 463.989649
## iter 30 value 463.947544
## final value 463.947516
## converged
##
## Step: AIC=983.9
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + EDUCATION_2_TEXT +
## neuroticism_qual + extraversion_qual + openness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 485.022400
## iter 20 value 482.589677
## final value 482.570082
## converged
## trying - HAS_LIVED_USA
## # weights: 42 (26 variable)
## initial value 716.295212
## iter 10 value 473.972602
## iter 20 value 466.540777
## iter 30 value 466.486440
## iter 30 value 466.486438
## iter 30 value 466.486438
## final value 466.486438
## converged
## trying - EDUCATION_2_TEXT
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 471.321804
## iter 20 value 467.003348
## final value 466.990444
## converged
## trying - neuroticism_qual
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 472.893712
## iter 20 value 468.651939
## final value 468.633049
## converged
## trying - extraversion_qual
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 475.366755
## iter 20 value 467.669826
## final value 467.635714
## converged
## trying - openness_qual
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 479.544315
## iter 20 value 474.993304
## final value 474.952664
## converged
## trying - agreeableness_qual
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 482.410024
## iter 20 value 476.468661
## final value 476.446333
## converged
## Df AIC
## - EDUCATION_2_TEXT 24 981.9809
## - extraversion_qual 24 983.2714
## <none> 28 983.8950
## - HAS_LIVED_USA 26 984.9729
## - neuroticism_qual 24 985.2661
## - openness_qual 24 997.9053
## - agreeableness_qual 24 1000.8927
## - EXPGRP_TEXT 24 1013.1402
## # weights: 39 (24 variable)
## initial value 716.295212
## iter 10 value 471.321804
## iter 20 value 467.003348
## final value 466.990444
## converged
##
## Step: AIC=981.98
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual +
## extraversion_qual + openness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 489.150703
## iter 20 value 487.391804
## final value 487.383516
## converged
## trying - HAS_LIVED_USA
## # weights: 36 (22 variable)
## initial value 716.295212
## iter 10 value 477.377240
## iter 20 value 469.976309
## final value 469.943756
## converged
## trying - neuroticism_qual
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 474.911609
## iter 20 value 471.470110
## final value 471.465553
## converged
## trying - extraversion_qual
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 476.402111
## iter 20 value 470.388840
## final value 470.381790
## converged
## trying - openness_qual
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 482.098263
## iter 20 value 478.371906
## final value 478.366797
## converged
## trying - agreeableness_qual
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 485.621098
## iter 20 value 480.607089
## final value 480.602495
## converged
## Df AIC
## - extraversion_qual 20 980.7636
## <none> 24 981.9809
## - neuroticism_qual 20 982.9311
## - HAS_LIVED_USA 22 983.8875
## - openness_qual 20 996.7336
## - agreeableness_qual 20 1001.2050
## - EXPGRP_TEXT 20 1014.7670
## # weights: 33 (20 variable)
## initial value 716.295212
## iter 10 value 476.402111
## iter 20 value 470.388840
## final value 470.381790
## converged
##
## Step: AIC=980.76
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual +
## openness_qual + agreeableness_qual
##
## trying - EXPGRP_TEXT
## # weights: 27 (16 variable)
## initial value 716.295212
## iter 10 value 492.680659
## iter 20 value 489.432376
## final value 489.432340
## converged
## trying - HAS_LIVED_USA
## # weights: 30 (18 variable)
## initial value 716.295212
## iter 10 value 482.915879
## iter 20 value 473.412742
## final value 473.410453
## converged
## trying - neuroticism_qual
## # weights: 27 (16 variable)
## initial value 716.295212
## iter 10 value 478.676191
## iter 20 value 475.142942
## final value 475.142866
## converged
## trying - openness_qual
## # weights: 27 (16 variable)
## initial value 716.295212
## iter 10 value 487.526012
## iter 20 value 482.434037
## final value 482.433840
## converged
## trying - agreeableness_qual
## # weights: 27 (16 variable)
## initial value 716.295212
## iter 10 value 491.485512
## iter 20 value 484.121239
## final value 484.121220
## converged
## Df AIC
## <none> 20 980.7636
## - neuroticism_qual 16 982.2857
## - HAS_LIVED_USA 18 982.8209
## - openness_qual 16 996.8677
## - agreeableness_qual 16 1000.2424
## - EXPGRP_TEXT 16 1010.8647## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA +
## neuroticism_qual + openness_qual + agreeableness_qual, data = df_covmis2)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite HAS_LIVED_USATRUE
## 1 1.549863 -1.3472769 -0.9471874 0.4436547
## 3 -2.276530 0.2021828 0.9919668 0.6868963
## neuroticism_qualmiddle neuroticism_qualhigh openness_qualmiddle
## 1 0.8052479 0.8908038 -0.4240645
## 3 0.7455313 0.2827003 -0.7400050
## openness_qualhigh agreeableness_qualmiddle agreeableness_qualhigh
## 1 0.5493604 -1.0754982 0.2634294
## 3 -0.6786259 -0.0199739 0.6409300
##
## Residual Deviance: 940.7636
## AIC: 980.7636regm <- multinom(covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA +
SEX_TEXT + openness_qual + agreeableness_qual,
data = df_covmis2)## # weights: 36 (22 variable)
## initial value 716.295212
## iter 10 value 475.607172
## iter 20 value 468.565066
## iter 30 value 468.414007
## final value 468.413496
## convergedstargazer::stargazer(regm)##
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jun 07, 2022 - 15:13:14
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lcc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## & \multicolumn{2}{c}{\textit{Dependent variable:}} \\
## \cline{2-3}
## \\[-1.8ex] & 1 & 3 \\
## \\[-1.8ex] & (1) & (2)\\
## \hline \\[-1.8ex]
## EXPGRP\_TEXTNon-Chinese Asian & $-$1.388$^{**}$ & 0.168 \\
## & (0.624) & (1.177) \\
## & & \\
## EXPGRP\_TEXTWhite & $-$0.922$^{***}$ & 0.983$^{**}$ \\
## & (0.231) & (0.487) \\
## & & \\
## HAS\_LIVED\_USA & 0.398$^{*}$ & 0.651$^{*}$ \\
## & (0.209) & (0.354) \\
## & & \\
## SEX\_TEXTMale & $-$0.650$^{***}$ & $-$0.377 \\
## & (0.201) & (0.333) \\
## & & \\
## SEX\_TEXTOther & 13.803$^{***}$ & $-$2.293$^{***}$ \\
## & (0.00000) & (0.000) \\
## & & \\
## SEX\_TEXTTransgender & 0.059 & $-$12.092$^{***}$ \\
## & (1.181) & (0.00000) \\
## & & \\
## openness\_qualmiddle & $-$0.446 & $-$0.744 \\
## & (0.470) & (0.671) \\
## & & \\
## openness\_qualhigh & 0.534 & $-$0.700 \\
## & (0.497) & (0.725) \\
## & & \\
## agreeableness\_qualmiddle & $-$1.272$^{**}$ & 0.069 \\
## & (0.583) & (1.151) \\
## & & \\
## agreeableness\_qualhigh & $-$0.315 & 0.501 \\
## & (0.630) & (1.209) \\
## & & \\
## Constant & 2.865$^{***}$ & $-$1.569 \\
## & (0.738) & (1.334) \\
## & & \\
## \hline \\[-1.8ex]
## Akaike Inf. Crit. & 980.827 & 980.827 \\
## \hline
## \hline \\[-1.8ex]
## \textit{Note:} & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
## \end{tabular}
## \end{table}ggcoef_multinom(regm, exponentiate = TRUE)
Let’s reduce the size of our data
df_covmis$EDUCATION_2_TEXT <- fct_recode(df_covmis$EDUCATION_1 %>% as.character,
"No college degree"="1",
"No college degree"="2",
"No college degree"="3",
"College degree"="4",
"College degree"="5",
"Graduate degree"="6",
"Graduate degree"="7",
"Graduate degree"="8")
df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
"-25y"="(1995,2005]",
"25 - 45y"="(1985,1995]",
"25 - 45y"="(1975,1985]",
"+45y"="(1944,1955]",
"+45y"="(1955,1965]",
"+45y"="(1965,1975]") %>%
relevel("-25y")
df_covmis$HH_INCOME_TEXT2 <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $30,000"="1",
"Less than $30,000"="2",
"$30,000 to $70,000"="3",
"$30,000 to $70,000"="4",
"$70,000 or more"="5",
"$70,000 or more"="6",
"$70,000 or more"="7",
"$70,000 or more"="8")
df_covmis$covqual_class <- relevel(df_covmis$covqual_class, "1")
df_covmis3 <- df_covmis %>%
filter(EXPGRP_TEXT != "Non-Chinese Asian" &
!(CONTINENT_BORN_TEXT_1 %in% c("4Tigers and Japan", "Africa", "Middle East",
"North America", "Oceania", "South America")) &
!(is.na(CONTINENT_BORN_TEXT_1)) &
!(SEX_TEXT %in% c("Other", "Transgender")) &
HH_INCOME_TEXT != "$500,000 or more")
df_covmis3$CONTINENT_BORN_TEXT_1 <- df_covmis3$CONTINENT_BORN_TEXT_1 %>% as.character %>% as.factor %>% relevel("USA")
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + DOB_AGE_BRACKET + SEX_TEXT + EDUCATION_2_TEXT + HH_INCOME_TEXT2 + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual,
data = df_covmis3)## # weights: 69 (44 variable)
## initial value 653.674312
## iter 10 value 460.900377
## iter 20 value 412.307293
## iter 30 value 407.613770
## iter 40 value 407.525232
## final value 407.524572
## convergedsummary(regm)## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 +
## DOB_AGE_BRACKET + SEX_TEXT + EDUCATION_2_TEXT + HH_INCOME_TEXT2 +
## neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual +
## agreeableness_qual, data = df_covmis3)
##
## Coefficients:
## (Intercept) EXPGRP_TEXTWhite CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2 -2.447998 0.8705434 0.48914147
## 3 -4.280277 2.6819800 0.01464403
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Western Europe
## 2 -0.05794654 -0.2793198
## 3 0.92692798 -1.1806136
## DOB_AGE_BRACKET+45y DOB_AGE_BRACKET25 - 45y SEX_TEXTMale
## 2 0.09266112 0.003424176 0.6742221
## 3 -0.68463859 0.115706962 -0.1579060
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2 -0.3176707 -0.2596761
## 3 -0.2561158 -1.2901992
## HH_INCOME_TEXT2$30,000 to $70,000 HH_INCOME_TEXT2$70,000 or more
## 2 -0.1264863 -0.6275616
## 3 -0.1333308 -0.4464852
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 2 -0.9303059 -0.7018127 0.3483925
## 3 -0.1047777 -0.3943623 0.5141155
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 2 -0.04922208 0.6587268 -0.5093904
## 3 0.60811448 -0.5808039 -1.5987915
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 2 0.1704969 0.2422436
## 3 0.4946263 1.1397106
## agreeableness_qualmiddle agreeableness_qualhigh
## 2 1.1897790 0.0910851
## 3 0.8669669 0.1789035
##
## Std. Errors:
## (Intercept) EXPGRP_TEXTWhite CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2 0.9800527 0.3272734 0.3730280
## 3 1.6409939 0.7759154 0.5533785
## CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Western Europe
## 2 0.4699871 0.3234785
## 3 1.0337213 0.5334005
## DOB_AGE_BRACKET+45y DOB_AGE_BRACKET25 - 45y SEX_TEXTMale
## 2 0.4202992 0.2708421 0.2383787
## 3 0.6631363 0.4049085 0.3683903
## EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2 0.2646574 0.3511567
## 3 0.3884076 0.6344856
## HH_INCOME_TEXT2$30,000 to $70,000 HH_INCOME_TEXT2$70,000 or more
## 2 0.2651938 0.3041295
## 3 0.4110522 0.4722572
## neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 2 0.3633538 0.4323644 0.3134390
## 3 0.5332415 0.6700849 0.4919758
## extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 2 0.4569988 0.5165668 0.5477978
## 3 0.6621812 0.6594343 0.7113076
## conscientiousness_qualmiddle conscientiousness_qualhigh
## 2 0.3557977 0.4392429
## 3 0.6302087 0.6898526
## agreeableness_qualmiddle agreeableness_qualhigh
## 2 0.629212 0.7085308
## 3 1.119453 1.2072278
##
## Residual Deviance: 815.0491
## AIC: 903.0491table(df_covmis$EDUCATION_2_TEXT, df_covmis$EXPGRP_TEXT) %>% lprop() %>% xtable::xtable()## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun 7 15:13:17 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
## \hline
## & Chinese & Non-Chinese Asian & White & Total \\
## \hline
## No college degree & 27.17 & 0.72 & 72.10 & 100.00 \\
## College degree & 36.19 & 2.72 & 61.09 & 100.00 \\
## Graduate degree & 44.19 & 4.65 & 51.16 & 100.00 \\
## All & 33.99 & 2.27 & 63.75 & 100.00 \\
## \hline
## \end{tabular}
## \end{table}ggcoef_multinom(
regm,
exponentiate = TRUE
)
Create a quantitative variable of covid skeptism
options: - Sem scoring on latent variable - Coordinate on the first variable of MFA which resume almost 50% of the variance - Standardization of score
SEM Scoring for covid skepticism
library(questionr)
library(FactoMineR)
library(tidyverse)
library(lavaan)## This is lavaan 0.6-11
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covlibrary(semTools)## ## ################################################################################# This is semTools 0.5-6## All users of R (or SEM) are invited to submit functions or ideas for functions.## #################################################################################
## Attaching package: 'semTools'## The following objects are masked from 'package:psych':
##
## reliability, skew## The following object is masked from 'package:readr':
##
## clipboardlibrary(lavaanPlot)
library(MVN)
library(MIIVsem)## This is MIIVsem 0.5.8## MIIVsem is BETA software! Please report any bugs.options(max.print=2000)’ Attention =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt Conspiracy =~ covmis_cnsp_ctiusAsian + covmis_cnsp_stpCovStpImmi + covmis_cnsp_redIntWthChina + covmis_cnsp_chnsCovRcst Origin =~ covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov Politics =~ covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr Coverage =~ covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd AntiVacc =~ covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus MedSkep =~ covmis_mdsk_medOrgUntrust + covmis_mdsk_skeptInfoDocSci + covmis_mdsk_medOrgRecBstInt + covmis_mdsk_fllwRecMedOrgImp Attention ~~ Conspiracy Attention ~~ Origin Attention ~~ Politics Attention ~~ Coverage Attention ~~ AntiVacc Attention ~~ MedSkep Conspiracy ~~ Origin Conspiracy ~~ Politics Conspiracy ~~ Coverage Conspiracy ~~ AntiVacc Conspiracy ~~ MedSkep Origin ~~ Politics Origin ~~ Coverage Origin ~~ AntiVacc Origin ~~ MedSkep Politics ~~ Coverage Politics ~~ AntiVacc Politics ~~ MedSkep Coverage ~~ AntiVacc Coverage ~~ MedSkep AntiVacc ~~ MedSkep CovidSkepticism =~ Attention + Conspiracy + Origin + Politics + Coverage + AntiVacc + MedSkep ’
model <- '
CovidSkepticism =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt + covmis_cnsp_ctiusAsian + covmis_cnsp_stpCovStpImmi + covmis_cnsp_redIntWthChina + covmis_cnsp_chnsCovRcst + covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov + covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr + covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd + covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus + covmis_mdsk_medOrgUntrust + covmis_mdsk_skeptInfoDocSci + covmis_mdsk_medOrgRecBstInt + covmis_mdsk_fllwRecMedOrgImp
'fit <- cfa(model, data = df_covmis, estimator = "ML")
semPlot::semPaths(fit)
summary(fit, fit.measures=T, standardized=T, rsquare=T) ## lavaan 0.6-11 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations 662
##
## Model Test User Model:
##
## Test statistic 3064.483
## Degrees of freedom 299
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 9296.673
## Degrees of freedom 325
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.692
## Tucker-Lewis Index (TLI) 0.665
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -26184.534
## Loglikelihood unrestricted model (H1) -24652.292
##
## Akaike (AIC) 52473.067
## Bayesian (BIC) 52706.821
## Sample-size adjusted Bayesian (BIC) 52541.719
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.118
## 90 Percent confidence interval - lower 0.114
## 90 Percent confidence interval - upper 0.122
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.084
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CovidSkepticism =~
## covmis_att_flu 1.000 0.573 0.378
## covmis_att_frD 0.685 0.124 5.527 0.000 0.393 0.254
## cvms_tt_ldrNBD 0.876 0.107 8.185 0.000 0.502 0.480
## cvms_tt_rrNWrr 0.994 0.113 8.790 0.000 0.570 0.569
## cvms_tt_bgThrt 0.943 0.132 7.133 0.000 0.541 0.370
## cvms_cnsp_ctsA 0.525 0.085 6.158 0.000 0.301 0.295
## cvms_cnsp_sCSI 0.951 0.132 7.213 0.000 0.545 0.377
## cvms_cnsp_rIWC 1.169 0.145 8.039 0.000 0.670 0.462
## cvms_cnsp_chCR 1.426 0.175 8.166 0.000 0.818 0.478
## cvms_rgn_cvPln 1.482 0.155 9.543 0.000 0.850 0.732
## covms_rgn_cvNt 1.413 0.160 8.841 0.000 0.810 0.577
## cvms_rgn_cvNgL 1.711 0.182 9.398 0.000 0.981 0.694
## cvms_rgn_scFNC 1.743 0.182 9.553 0.000 0.999 0.735
## cvms_pltc_pBDI 1.727 0.199 8.683 0.000 0.990 0.551
## cvms_pltc_NSPS 1.764 0.184 9.562 0.000 1.012 0.737
## cvms_plt_DCPLD 1.297 0.144 9.033 0.000 0.744 0.613
## cvms_cvrg_mCBD 1.932 0.205 9.410 0.000 1.108 0.697
## cvms_cvrg_GJCC 0.652 0.105 6.195 0.000 0.374 0.298
## cvms_c_UCMTRLB 1.723 0.187 9.238 0.000 0.988 0.655
## cvms_nt_fGUCMV 1.812 0.190 9.524 0.000 1.039 0.727
## cvms_nt_thnNCV 1.168 0.126 9.296 0.000 0.670 0.669
## cvms_nt_cvVERV 1.412 0.152 9.315 0.000 0.810 0.673
## cvms_mdsk_mdOU 1.755 0.186 9.443 0.000 1.006 0.705
## cvms_mdsk_sIDS 1.658 0.171 9.719 0.000 0.951 0.786
## cvms_mdsk_ORBI 1.513 0.162 9.334 0.000 0.867 0.678
## cvms_mdsk_RMOI 1.452 0.152 9.574 0.000 0.833 0.741
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .covmis_att_flu 1.970 0.109 18.017 0.000 1.970 0.857
## .covmis_att_frD 2.232 0.123 18.120 0.000 2.232 0.935
## .cvms_tt_ldrNBD 0.841 0.047 17.876 0.000 0.841 0.769
## .cvms_tt_rrNWrr 0.680 0.038 17.688 0.000 0.680 0.677
## .cvms_tt_bgThrt 1.842 0.102 18.025 0.000 1.842 0.863
## .cvms_cnsp_ctsA 0.951 0.053 18.093 0.000 0.951 0.913
## .cvms_cnsp_sCSI 1.791 0.099 18.018 0.000 1.791 0.858
## .cvms_cnsp_rIWC 1.653 0.092 17.906 0.000 1.653 0.786
## .cvms_cnsp_chCR 2.258 0.126 17.880 0.000 2.258 0.772
## .cvms_rgn_cvPln 0.625 0.037 16.969 0.000 0.625 0.464
## .covms_rgn_cvNt 1.314 0.074 17.665 0.000 1.314 0.667
## .cvms_rgn_cvNgL 1.039 0.060 17.212 0.000 1.039 0.519
## .cvms_rgn_scFNC 0.850 0.050 16.949 0.000 0.850 0.460
## .cvms_pltc_pBDI 2.252 0.127 17.733 0.000 2.252 0.697
## .cvms_pltc_NSPS 0.858 0.051 16.930 0.000 0.858 0.456
## .cvms_plt_DCPLD 0.920 0.052 17.557 0.000 0.920 0.625
## .cvms_cvrg_mCBD 1.302 0.076 17.195 0.000 1.302 0.515
## .cvms_cvrg_GJCC 1.439 0.080 18.091 0.000 1.439 0.911
## .cvms_c_UCMTRLB 1.296 0.075 17.396 0.000 1.296 0.570
## .cvms_nt_fGUCMV 0.964 0.057 17.008 0.000 0.964 0.472
## .cvms_nt_thnNCV 0.554 0.032 17.337 0.000 0.554 0.553
## .cvms_nt_cvVERV 0.791 0.046 17.316 0.000 0.791 0.547
## .cvms_mdsk_mdOU 1.024 0.060 17.147 0.000 1.024 0.503
## .cvms_mdsk_sIDS 0.559 0.034 16.479 0.000 0.559 0.382
## .cvms_mdsk_ORBI 0.886 0.051 17.295 0.000 0.886 0.541
## .cvms_mdsk_RMOI 0.569 0.034 16.903 0.000 0.569 0.451
## CovidSkepticsm 0.329 0.067 4.918 0.000 1.000 1.000
##
## R-Square:
## Estimate
## covmis_att_flu 0.143
## covmis_att_frD 0.065
## cvms_tt_ldrNBD 0.231
## cvms_tt_rrNWrr 0.323
## cvms_tt_bgThrt 0.137
## cvms_cnsp_ctsA 0.087
## cvms_cnsp_sCSI 0.142
## cvms_cnsp_rIWC 0.214
## cvms_cnsp_chCR 0.228
## cvms_rgn_cvPln 0.536
## covms_rgn_cvNt 0.333
## cvms_rgn_cvNgL 0.481
## cvms_rgn_scFNC 0.540
## cvms_pltc_pBDI 0.303
## cvms_pltc_NSPS 0.544
## cvms_plt_DCPLD 0.375
## cvms_cvrg_mCBD 0.485
## cvms_cvrg_GJCC 0.089
## cvms_c_UCMTRLB 0.430
## cvms_nt_fGUCMV 0.528
## cvms_nt_thnNCV 0.447
## cvms_nt_cvVERV 0.453
## cvms_mdsk_mdOU 0.497
## cvms_mdsk_sIDS 0.618
## cvms_mdsk_ORBI 0.459
## cvms_mdsk_RMOI 0.549fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter(!(rhs%in%lhs)) %>%
group_by(lhs) %>%
summarise(sest=sum(est)) %>%
inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter((rhs%in%lhs)) %>%
dplyr::select(rhs,est),
by = c("lhs" = "rhs")) %>%
mutate(factor=sest*est) %>%
dplyr::select(factor) %>%
colSums()## factor
## 0f_semscoring_latent <- function(fit, data, scaled = TRUE){
df_sem_estimate <- fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1)
v_latent_var <- df_sem_estimate$lhs %>% as_factor %>% levels
f_calculate_latent <- function(latent_var, data2){
v_obs_var <- df_sem_estimate %>% filter(lhs==latent_var) %>% dplyr::select(rhs) %>% as_vector()
d <- tibble(matrix(ncol = 0, nrow = nrow(data)))
for (obs_var in v_obs_var) {
res <- data2[,obs_var] * (df_sem_estimate %>% filter(lhs==latent_var, rhs==obs_var) %>% dplyr::select(est) %>% as.numeric())
d[,obs_var] <- res
}
res <- rowSums(d)
if(scaled) res <- res %>% scale
return(res)
}
d <- tibble(matrix(ncol = 0, nrow = nrow(data)))
for(j in v_latent_var){
data[,j] <- f_calculate_latent(j, data)
}
data <- data %>% dplyr::select(v_latent_var)
return(data)
}
df_scorecovmis <- f_semscoring_latent(fit, df_covmis, scaled = F)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(v_latent_var)` instead of `v_latent_var` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.(df_scorecovmis$CovidSkepticism == min(df_scorecovmis$CovidSkepticism)) %>% which()## [1] 455 589df_covmis %>% dplyr::select(matches("^covmis")) %>% as_tibble %>% dplyr::slice(455L,589L)## # A tibble: 2 × 26
## covmis_att_flu covmis_att_afrDie covmis_att_eldrNoBgDl covmis_att_rareNoWorr
## <int> <dbl> <int> <int>
## 1 1 1 1 1
## 2 1 1 1 1
## # … with 22 more variables: covmis_att_bgThrt <dbl>,
## # covmis_cnsp_ctiusAsian <int>, covmis_cnsp_stpCovStpImmi <int>,
## # covmis_cnsp_redIntWthChina <int>, covmis_cnsp_chnsCovRcst <dbl>,
## # covmis_orgn_covPlnnd <int>, covmis_orgn_covNat <dbl>,
## # covmis_orgn_covNgeenLab <int>, covmis_orgn_scntFkNwsCov <int>,
## # covmis_pltc_polBgDlIntrst <int>, covmis_pltc_covNtSerPolSay <int>,
## # covmis_pltc_polDwnplCovPlpLDngr <dbl>, covmis_cvrg_mdiaCovBgrDl <int>, …standardisation_hierar <- function(x){
x <- (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter(!(rhs%in%lhs)) %>%
group_by(lhs) %>%
summarise(sest=sum(est)) %>%
inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter((rhs%in%lhs)) %>%
dplyr::select(rhs,est),
by = c("lhs" = "rhs")) %>%
mutate(factor=sest*est) %>%
dplyr::select(factor) %>%
colSums())) / (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter(!(rhs%in%lhs)) %>%
group_by(lhs) %>%
summarise(sest=sum(est)) %>%
inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter((rhs%in%lhs)) %>%
dplyr::select(rhs,est),
by = c("lhs" = "rhs")) %>%
mutate(factor=sest*est) %>%
dplyr::select(factor) %>%
colSums() * 6) * 100
}
standardisation <- function(x){
x <- (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
dplyr::select(est) %>%
colSums())) / ((fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
dplyr::select(est) %>%
colSums()) * 6) * 100
}
df_scorecovmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism %>% standardisation()
df_covmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism
df_covmis$CovidSkepticism %>% summary## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 8.982 17.231 20.404 29.763 75.678df_covmis$CovidSkepticism %>% hist()
write.csv(df_covmis,"./data/mjolnir_clean_v6_Covmis.csv")cleaning data
Some group of participants are not big enough to be analyzed and produces noises we want to avoid.
df_covmis$HH_INCOME_TEXT2 <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $30,000"="1",
"Less than $30,000"="2",
"$30,000 to $70,000"="3",
"$30,000 to $70,000"="4",
"$70,000 or more"="5",
"$70,000 or more"="6",
"$70,000 or more"="7",
"$70,000 or more"="8")
df_covmis$EDUCATION_2_TEXT <- fct_recode(df$EDUCATION_1 %>% as.character,
"No college degree"="1",
"No college degree"="2",
"No college degree"="3",
"College degree"="4",
"College degree"="5",
"Graduate degree"="6",
"Graduate degree"="7",
"Graduate degree"="8")
df_covmis$DOB_YEAR_PERIODE <- df_covmis$DOB_YEAR %>% cut(breaks = c(1944,1955,1965,1975,1985,1995,2005))
df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
"-25y"="(1995,2005]",
"25 - 35y"="(1985,1995]",
"35 - 45y"="(1975,1985]",
"+45y"="(1944,1955]",
"+45y"="(1955,1965]",
"+45y"="(1965,1975]")
f_normfactor <- function(v){
res <- AMBI::f_normalisation(v)
res <- res*3
res <- cut(res, c(-0.1,1,2,3), labels=c('weak','middle','high'))
return(res)
}
df_covmis$neuroticism_qual <- f_normfactor(df_covmis$AMBI_BIG5_Neuroticism)
df_covmis$extraversion_qual <- f_normfactor(df_covmis$AMBI_BIG5_Extraversion)
df_covmis$openness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Openness)
df_covmis$conscientiousness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Conscientiousness)
df_covmis$agreeableness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Agreeableness)
df_covmis2 <- df_covmis %>%
filter(EXPGRP_TEXT != "Non-Chinese Asian" &
!(CONTINENT_BORN_TEXT_1 %in% c("4Tigers and Japan", "Africa", "Middle East",
"North America", "Oceania", "South America")) &
!(is.na(CONTINENT_BORN_TEXT_1)) &
!(SEX_TEXT %in% c("Other", "Transgender")) &
HH_INCOME_TEXT != "$500,000 or more")
df_covmis2 %>%
dplyr::select(EXPGRP_TEXT) %>%
freq()## n % val%
## Chinese 195 32.6 32.6
## White 404 67.4 67.4df_covmis2 %>%
dplyr::select(CONTINENT_BORN_TEXT_1) %>%
freq()## n % val%
## USA 361 60.3 60.3
## 4Tigers and Japan 0 0.0 0.0
## Africa 0 0.0 0.0
## Central Eastern Europe 76 12.7 12.7
## Developping Asia 60 10.0 10.0
## Middle East 0 0.0 0.0
## North America 0 0.0 0.0
## Oceania 0 0.0 0.0
## South America 0 0.0 0.0
## Western Europe 102 17.0 17.0df_covmis2 %>%
dplyr::select(SEX_TEXT) %>%
freq()## n % val%
## Female 334 55.8 55.8
## Male 265 44.2 44.2df_covmis2 %>%
dplyr::select(agreeableness_qual) %>%
freq()## n % val%
## weak 24 4.0 4.0
## middle 404 67.4 67.4
## high 171 28.5 28.5df_covmis2 %>%
dplyr::select(conscientiousness_qual) %>%
freq()## n % val%
## weak 76 12.7 12.7
## middle 355 59.3 59.3
## high 168 28.0 28.0ex <- car::powerTransform(df_covmis2$CovidSkepticism+0.0000001)
df_covmis2$CovidSkepticism^ex$lambda %>% hist()
df_covmis2$CovidSkepticismnorm <- df_covmis2$CovidSkepticism^ex$lambdadf_covmis2 %>%
gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
ggplot(aes(Covmis_res, CovidSkepticism)) +
geom_point() +
geom_smooth(method = "loess") +
facet_wrap(~ Covmis_var, ncol = 5)## `geom_smooth()` using formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.3405e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.3405e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.118e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.118e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.000625## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 8.8071e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 8.8071e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.7365e-30## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.7365e-30## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.5791e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.5791e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.3171e-30## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 4.3171e-30## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.5595e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 4.5595e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.3803e-30## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.3803e-30## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.0048e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.0048e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.6618e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.6618e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.3814e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.3814e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 3## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 3## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.4577e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.4577e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2436e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.2436e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.1297e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.1297e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2732e-15## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.2732e-15## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.5338e-30## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 7.5338e-30## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.2269e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.2269e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.1545e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.1545e-16## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.6576e-30## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.6576e-30## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 EXPGRP_TEXT 1 581 14.677 1.41e-04 * 0.025000
## 2 CONTINENT_BORN_TEXT_1 3 581 1.176 3.18e-01 0.006000
## 3 HH_INCOME_TEXT2 2 581 0.841 4.32e-01 0.003000
## 4 SEX_TEXT 1 581 0.504 4.78e-01 0.000866
## 5 EDUCATION_2_TEXT 2 581 4.251 1.50e-02 * 0.014000
## 6 DOB_AGE_BRACKET 3 581 1.423 2.35e-01 0.007000
## 7 AMBI_BIG5_Neuroticism 1 581 0.445 5.05e-01 0.000765
## 8 AMBI_BIG5_Extraversion 1 581 26.899 2.97e-07 * 0.044000
## 9 AMBI_BIG5_Openness 1 581 55.870 2.86e-13 * 0.088000
## 10 AMBI_BIG5_Conscientiousness 1 581 0.233 6.29e-01 0.000401
## 11 AMBI_BIG5_Agreeableness 1 581 22.109 3.22e-06 * 0.037000res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT:DOB_AGE_BRACKET, data = df_covmis2)## Coefficient covariances computed by hccm()## Note: model has aliased coefficients
## sums of squares computed by model comparisonres.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 EXPGRP_TEXT 1 580 2.211 0.138 0.004000
## 2 CONTINENT_BORN_TEXT_1 3 580 3.614 0.013 * 0.018000
## 3 HH_INCOME_TEXT2 2 580 0.103 0.902 0.000356
## 4 SEX_TEXT 1 580 10.893 0.001 * 0.018000
## 5 EDUCATION_2_TEXT:DOB_AGE_BRACKET 11 580 2.870 0.001 * 0.052000res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 AMBI_BIG5_Neuroticism 1 593 1.154 2.83e-01 0.002
## 2 AMBI_BIG5_Extraversion 1 593 24.199 1.13e-06 * 0.039
## 3 AMBI_BIG5_Openness 1 593 43.480 9.49e-11 * 0.068
## 4 AMBI_BIG5_Conscientiousness 1 593 2.485 1.16e-01 0.004
## 5 AMBI_BIG5_Agreeableness 1 593 31.869 2.56e-08 * 0.051pwc <- df_covmis2 %>% tukey_hsd(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual)
pwc## # A tibble: 35 × 9
## term group1 group2 null.value estimate conf.low conf.high p.adj
## * <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EXPGRP_TEXT Chine… White 0 0.271 0.134 0.408 1.15e-4
## 2 CONTINENT_BORN_… USA Centr… 0 0.480 0.219 0.740 1.55e-5
## 3 CONTINENT_BORN_… USA Devel… 0 0.0568 -0.231 0.344 9.57e-1
## 4 CONTINENT_BORN_… USA Weste… 0 0.113 -0.118 0.344 5.9 e-1
## 5 CONTINENT_BORN_… Centr… Devel… 0 -0.423 -0.779 -0.0666 1.25e-2
## 6 CONTINENT_BORN_… Centr… Weste… 0 -0.367 -0.679 -0.0540 1.39e-2
## 7 CONTINENT_BORN_… Devel… Weste… 0 0.0562 -0.279 0.392 9.73e-1
## 8 HH_INCOME_TEXT2 Less … $30,0… 0 -0.0242 -0.211 0.163 9.5 e-1
## 9 HH_INCOME_TEXT2 Less … $70,0… 0 -0.0760 -0.265 0.113 6.12e-1
## 10 HH_INCOME_TEXT2 $30,0… $70,0… 0 -0.0518 -0.241 0.137 7.96e-1
## # … with 25 more rows, and 1 more variable: p.adj.signif <chr>res.aov <- anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT , data = df_covmis2)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 EXPGRP_TEXT 1 597 12.324 0.000481 * 0.02pwc <- df_covmis2 %>% tukey_hsd(CovidSkepticismnorm ~ EXPGRP_TEXT)
pwc## # A tibble: 1 × 9
## term group1 group2 null.value estimate conf.low conf.high p.adj
## * <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EXPGRP_TEXT Chinese White 0 0.271 0.119 0.423 0.000481
## # … with 1 more variable: p.adj.signif <chr>res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ demo_class + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis2)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 demo_class 5 583 5.792 3.13e-05 * 0.047000
## 2 neuroticism_qual 2 583 0.400 6.71e-01 0.001000
## 3 extraversion_qual 2 583 2.223 1.09e-01 0.008000
## 4 openness_qual 2 583 23.936 1.02e-10 * 0.076000
## 5 conscientiousness_qual 2 583 0.220 8.03e-01 0.000753
## 6 agreeableness_qual 2 583 11.982 7.95e-06 * 0.039000df_covmis2 <- fastDummies::dummy_cols(df_covmis2, select_columns = c("EXPGRP_TEXT","CONTINENT_BORN_TEXT_1", 'HH_INCOME_TEXT2', "SEX_TEXT", "EDUCATION_2_TEXT", "DOB_AGE_BRACKET"))
colnames(df_covmis2) <- str_replace_all(colnames(df_covmis2), "[ ,+$-]", "")
res.aov <- anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT_Chinese + EXPGRP_TEXT_White + CONTINENT_BORN_TEXT_1_CentralEasternEurope + CONTINENT_BORN_TEXT_1_DeveloppingAsia + CONTINENT_BORN_TEXT_1_USA + CONTINENT_BORN_TEXT_1_WesternEurope + HH_INCOME_TEXT2_Lessthan30000 + HH_INCOME_TEXT2_30000to70000 + HH_INCOME_TEXT2_70000ormore + SEX_TEXT_Female + SEX_TEXT_Male + EDUCATION_2_TEXT_Nocollegedegree + EDUCATION_2_TEXT_Collegedegree + EDUCATION_2_TEXT_Graduatedegree + DOB_AGE_BRACKET_45y + DOB_AGE_BRACKET_3545y + DOB_AGE_BRACKET_2535y + DOB_AGE_BRACKET_25y + AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)## Coefficient covariances computed by hccm()## Note: model has aliased coefficients
## sums of squares computed by model comparisonres.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05
## 1 EXPGRP_TEXT_Chinese 0 581 NA NA <NA>
## 2 EXPGRP_TEXT_White 0 581 NA NA <NA>
## 3 CONTINENT_BORN_TEXT_1_CentralEasternEurope 0 581 NA NA <NA>
## 4 CONTINENT_BORN_TEXT_1_DeveloppingAsia 0 581 NA NA <NA>
## 5 CONTINENT_BORN_TEXT_1_USA 0 581 NA NA <NA>
## 6 CONTINENT_BORN_TEXT_1_WesternEurope 0 581 NA NA <NA>
## 7 HH_INCOME_TEXT2_Lessthan30000 0 581 NA NA <NA>
## 8 HH_INCOME_TEXT2_30000to70000 0 581 NA NA <NA>
## 9 HH_INCOME_TEXT2_70000ormore 0 581 NA NA <NA>
## 10 SEX_TEXT_Female 0 581 NA NA <NA>
## 11 SEX_TEXT_Male 0 581 NA NA <NA>
## 12 EDUCATION_2_TEXT_Nocollegedegree 0 581 NA NA <NA>
## 13 EDUCATION_2_TEXT_Collegedegree 0 581 NA NA <NA>
## 14 EDUCATION_2_TEXT_Graduatedegree 0 581 NA NA <NA>
## 15 DOB_AGE_BRACKET_45y 0 581 NA NA <NA>
## 16 DOB_AGE_BRACKET_3545y 0 581 NA NA <NA>
## 17 DOB_AGE_BRACKET_2535y 0 581 NA NA <NA>
## 18 DOB_AGE_BRACKET_25y 0 581 NA NA <NA>
## 19 AMBI_BIG5_Neuroticism 1 581 0.445 5.05e-01
## 20 AMBI_BIG5_Extraversion 1 581 26.899 2.97e-07 *
## 21 AMBI_BIG5_Openness 1 581 55.870 2.86e-13 *
## 22 AMBI_BIG5_Conscientiousness 1 581 0.233 6.29e-01
## 23 AMBI_BIG5_Agreeableness 1 581 22.109 3.22e-06 *
## ges
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## 8 NA
## 9 NA
## 10 NA
## 11 NA
## 12 NA
## 13 NA
## 14 NA
## 15 NA
## 16 NA
## 17 NA
## 18 NA
## 19 0.000765
## 20 0.044000
## 21 0.088000
## 22 0.000401
## 23 0.037000df_covmis2$demo_class %>% freq()## n % val%
## 1 215 35.9 35.9
## 2 142 23.7 23.7
## 3 57 9.5 9.5
## 4 0 0.0 0.0
## 5 10 1.7 1.7
## 6 110 18.4 18.4
## 7 65 10.9 10.9What if without conspiracy and xenophobia question
model <- '
Attention =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt
Origin =~ covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov
Politics =~ covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr
Coverage =~ covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd
AntiVacc =~ covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus
CovidSkepticism2 =~ Attention + Origin + Politics + Coverage + AntiVacc
'
fit <- cfa(model, data = df_covmis, estimator = "ML")## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negativedf_scorecovmis <- f_semscoring_latent(fit, df_covmis, scaled = F)
standardisation <- function(x){
x <- (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter(!(rhs%in%lhs)) %>%
group_by(lhs) %>%
summarise(sest=sum(est)) %>%
inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter((rhs%in%lhs)) %>%
dplyr::select(rhs,est),
by = c("lhs" = "rhs")) %>%
mutate(factor=sest*est) %>%
dplyr::select(factor) %>%
colSums())) / (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter(!(rhs%in%lhs)) %>%
group_by(lhs) %>%
summarise(sest=sum(est)) %>%
inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
filter((rhs%in%lhs)) %>%
dplyr::select(rhs,est),
by = c("lhs" = "rhs")) %>%
mutate(factor=sest*est) %>%
dplyr::select(factor) %>%
colSums() * 6) * 100
}
df_scorecovmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism %>% standardisation
df_scorecovmis$CovidSkepticism %>% summary## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 9.774 18.800 21.822 30.392 76.180ex <- car::powerTransform(df_scorecovmis$CovidSkepticism+0.0000001)
df_scorecovmis$CovidSkepticism^ex$lambda %>% hist()
df_scorecovmis$CovidSkepticismnorm <- df_scorecovmis$CovidSkepticism^ex$lambda
df_covmis$CovidSkepticism2 <- df_scorecovmis$CovidSkepticism
df_covmis$CovidSkepticism2norm <- df_scorecovmis$CovidSkepticism^ex$lambdares.aov <- anova_test(CovidSkepticism2norm ~ EXPGRP_TEXT:CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT + SEX_TEXT + EDUCATION_2_TEXT:DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)## Warning: NA detected in rows: 43,60,106,112,310.
## Removing this rows before the analysis.## Coefficient covariances computed by hccm()## Note: model has aliased coefficients
## sums of squares computed by model comparisonres.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 HH_INCOME_TEXT 7 608 0.390 9.08e-01 0.004
## 2 SEX_TEXT 3 608 1.935 1.23e-01 0.009
## 3 neuroticism_qual 2 608 1.491 2.26e-01 0.005
## 4 extraversion_qual 2 608 4.211 1.50e-02 * 0.014
## 5 openness_qual 2 608 17.502 4.07e-08 * 0.054
## 6 conscientiousness_qual 2 608 0.427 6.53e-01 0.001
## 7 agreeableness_qual 2 608 13.171 2.51e-06 * 0.042
## 8 EXPGRP_TEXT:CONTINENT_BORN_TEXT_1 17 608 2.760 1.88e-04 * 0.072
## 9 EDUCATION_2_TEXT:DOB_AGE_BRACKET 11 608 2.214 1.30e-02 * 0.039res.aov <- anova_test(CovidSkepticism2norm ~ demo_class + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)## Coefficient covariances computed by hccm()res.aov## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 demo_class 6 645 5.136 3.61e-05 * 0.046000
## 2 neuroticism_qual 2 645 1.014 3.63e-01 0.003000
## 3 extraversion_qual 2 645 3.202 4.10e-02 * 0.010000
## 4 openness_qual 2 645 19.120 8.58e-09 * 0.056000
## 5 conscientiousness_qual 2 645 0.037 9.64e-01 0.000115
## 6 agreeableness_qual 2 645 13.577 1.68e-06 * 0.040000##To do mediation analysis
write.csv(df_covmis,"./data/mjolnir_clean_v6_Covmis.csv")